PAWS Lab - User contributions [en]

Smart Content

2016-04-19T19:25:14Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

<html>
<h3>Annotated Examples Demonstration</h3>
<br>
<iframe src="http://adapt2.sis.pitt.edu/pitt/annotated/annotated-demo/foo?act=pyt3.2&sub=0" width="500" height="210" border="0"></iframe>
</html>

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Smart Content

2016-04-19T19:19:01Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

<html>
<iframe src="http://adapt2.sis.pitt.edu/pitt/annotated/annotated-demo/foo?act=pyt3.2&sub=0" width="500" height="210" border="0"></iframe>
</html>

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Smart Content

2016-04-19T19:17:15Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

<html>
<iframe src="http://adapt2.sis.pitt.edu/pitt/annotated/annotated-demo/foo?act=pyt3.2&sub=0" width="500" height="250" border="0"></iframe>
</html>

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Smart Content

2016-04-19T19:17:02Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

<html>
<iframe src="http://adapt2.sis.pitt.edu/pitt/annotated/annotated-demo/foo?act=pyt3.2&sub=0" width="500" height="300" border="0"></iframe>
</html>

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Smart Content

2016-04-19T19:16:33Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

<html>
<iframe src="http://adapt2.sis.pitt.edu/pitt/annotated/annotated-demo/foo?act=pyt3.2&sub=0" width="500" height="500"></iframe>
</html>

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Smart Content

2016-04-19T19:15:35Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

<html>
<iframe src="http://adapt2.sis.pitt.edu/pitt/annotated/annotated-demo/foo?act=pyt3.2&sub=0"></iframe>
</html>

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Smart Content

2016-04-19T03:10:38Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Smart Content

2016-04-19T03:08:34Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

<html>
This is <b>HTML</b>.
</html>

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Smart Content

2016-04-19T03:07:24Z

Hnvasa: /* Annotated Examples */

Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain semantics, problems, parson problems, annotated examples, and animated examples. This sections reviews all these content types.

While Mastery Grids interface can support any kind of content, we try to focus on "smart learning content" that interactively engages student, collects interaction data, and provides various kinds of feedback. For a discussion and definition of smart learning content, see the following ITiCSE Working Group Report:

Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) [https://www.researchgate.net/publication/273380880_Increasing_Adoption_of_Smart_Learning_Content_for_Computer_Science_Education Increasing Adoption of Smart Learning Content for Computer Science Education]. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57.

== Semantics Problems for Java and Python ==
We have developed a content application [[QuizJET]] providing problems to learn Java Programming. Here are two problems for Java. The left one shows a problem with a single class asking the value of a variable in a piece of code. The right one shows a problem with multiple classes asking the console output of a piece of code. Correctness is accessed by the content application and students can attempt multiple times with different instantiations of the variables in the problem. Similarly, we have developed a content application [[QuizPET]] providing the same type of problems to learn Python Programming.

New problems for [[QuizJET]] and [[QuizPET]] could be easily created with [[QuizJET Authoring System]] tool and [[QuizPET Authoring System]].

[[Image:Problems.jpg|650x1300px]]

== SQL Problems ==

For learning SQL, we have developed a content application [[SQLKnoT]] providing another type of problem. Students are asked to write a complete query to achieve a task in each problem. Here is a problem for SQL.

[[Image:Problems_Sql.jpg|500x750px]]

== Parson Problems for Python ==
Here is a parson problems for Python. In such problems, students are asked to drag different fragments of a code to construct a complete code in order to achieve a task. Some examples of created parson problems can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsparsons-python tab).

New parson problems can be created by [[Parson Problem Authoring System]].

[[Image:ParsonProblems.jpg|750x1000px]]

== Annotated Examples ==
We have developed a content application [[WebEx]] providing annotated examples. Students can click on the left side box to learn about each line’s related concepts.
New examples for Java, Python, SQL, C, and other programming languages could be created with [[Annotated Example Authoring System]].
[[JSWebEx]] is the new Javascript interface that replaces the old WebEx system to demonstrate the annotated examples. Here is an annotated examples for Python.

[[Image:jswebex.png|650x800px]]

<html>
This is <em>HTML</em>.
</html>

== Animated Examples for Python and Java ==
Here is an animated example for Python. In such examples, the line-by-line execution of a piece of code is visualized by animations. Some animated examples created can be found [http://acos.cs.hut.fi/ here] under "Installed content packages" (e.g. jsvee-python tab).

New animated example can be created by [[Animated Example Authoring System]].

[[Image:AnimatedExamples.jpg|750x1000px]]

Adaptive Navigation Support and Open Social Learner Modeling for PAL

2016-04-04T20:10:46Z

Hnvasa: /* Open Source */

== Overview ==

The goal of this project is to leverage the power of [[open social learner modeling]] and [[adaptive navigation support]] in the context of the envisioned Personalized Assistant for Learning (PAL). The project is supported by the [http://adlnet.gov Advanced Distributed Learning Initiative] contract W911QY13C0032. This is a joint project with [http://cs.aalto.fi/en/research/ Learning + Technology] research group at Aalto University. The group at Aalto University aims at producing smart learning content on Python programming, of which details can be accessed [http://acos.cs.hut.fi/ here] or from specific sections on this page.

The project focuses on both exploration and implementation of adaptive navigation support and open social learner modeling and pursues three directions of work:

* Exploring open social learner modeling interface for diverse learning content
* Enhancing algorithms for personalized guidance using knowledge-based and social approaches
* Developing architectural solutions and authoring tools to support open social learner modeling

== Open Social Learner Model Interface: Mastery Grids ==

{|
|valign="top" | [[Image:Mg_1.png|thumb|left|'''100'''|Mastery Grids Interface]]
|valign="top" | Our latest implementation of Open Social Learner Modeling (OSLM) is [[Mastery Grids Interface]]. Mastery Grids is both an innovative Open Social Learner Model Interface and an adaptive E-learning platform with integrated functionalities enabling multi-facet social comparison, open learner modeling, and adaptive navigation support to access multiple kinds of smart learning content. Mastery Grids is supported by adaptive social learning framework [[Aggregate]]. This framework supports several kinds of open student modeling, social comparison, and recommendation. In detail, Mastery Grids presents and compares user learning progress and knowledge level using colored grids, tracks user activities with learning content, and provides flexible user-centered navigation across different content levels (e.g. topic, question) and different content types (e.g. problem, example). Our past research shows that open student modeling and social comparison effectively increases students’ performance, motivation, engagement and retention.

* [[Mastery Grids Interface|More about Mastery Grids interface]]
* [http://adapt2.sis.pitt.edu/um-vis-adl/index.html?usr=adl01&grp=ADL&sid=test&cid=13&data-top-n-grp=5&def-val-rep-lvl-id=p&def-val-res-id=AVG&ui-tbar-rep-lvl-vis=0&ui-tbar-topic-size-vis=0 An interactive demo of Mastery Grids interface]
|}

== Architecture: Aggregate==
{|
|valign="top" | [[Image:arch_v2.png|thumb|left|'''100'''|Aggregate Architecture]]
|valign="top" | We developed an adaptive social learning architecture [[Aggregate]] to support Mastery Grids interface. [[Aggregate]] is an extension of our original [[ADAPT2]] architecture. On the top of [[ADAPT2]] , [[Aggregate]] architecture supports several kinds of open student modeling, social comparison, content brokering, and recommendation services. The architecture fulfills a major objective, portability, which is the ability to be integrated to other systems with little set up and modification. The architecture is modular and includes different software components.

* [[ADAPT2| More about ADAPT2]]
* [[Aggregate| More about Aggregate]]
|}

== Learner Model ==
{|
|valign="top" | [[Image:CUMULATE.evidence propagation.png|thumb|left|'''100'''|CUMULATE]]
|valign="center" | We have developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. CUMULATE allows flexible learner models to infer learner knowledge. Mastery Grids's architecture is supported by CUMULATE and thus it also supports flexible learner models. We have proposed and implemented different learner models over past years, including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]] which is the main one currently deployed in our systems, and [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance. The explanation of the communication between the interface and learner model can be found in [[Aggregate]].

* [[CUMULATE|More about CUMULATE]]
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]
|}

== Personalization ==
{|
|valign="top" | [[Image:starRecommendation.png|thumb|left|'''100'''|Personalization]]
|valign="top" | There are multiple personalization approaches, that are developed and researched in our system. In the form of recommendations, we have various methods in different levels for recommending learning material to students. Two major approached for recommending resources are reactive and proactive recommendations. In the reactive approach, the recommender system activates in reaction to the student's activity, e.g. if the student fails in solving a quiz, the reactive recommender system recommends related examples to this student to help her understand the skills required to solve that quiz. The pro-active recommender system, proactively suggests learning materials to the students.

|}

== Educational Data Mining ==
{|
|valign="top" | [[Image:TensorFactorization.png|thumb|left|'''100'''|Educational Data Mining]]
|valign="top" | In this part of the project, we aim to make sense of data from Mastery Grids system, including logs of student attempts. The goal in this part includes understanding students' learning patterns and its relationship with students' behavioral traits, predicting students' performance, modeling student knowledge, and discovering the content model. These tasks eventually help us in providing a better service to both instructors and students. [[Educational Data Mining|==> more]]

|}

== Smart Content ==
{|
|valign="top" | [[Image:AnimatedExamples.jpg|thumb|left|'''100'''|Smart Content (Animated Examples)]]
|valign="top" | Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. We have developed different content applications (e.g., [[QuizJET]], [[QuizPET]], [[SQLKnoT]], [[WebEx]]) and authoring tools (e.g., [[Content Authoring Tools]], [[Course Authoring Tool]], [[Group Authoring Tool]], [[Authoring Tool Portal]]) for accessing and authoring such contents. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain problems (quizzes), parson problems, annotated examples, and animated examples collected from experienced course teachers, textbooks or domain experts. [[Smart Content|==> more]]
|}

== Authoring Tools ==
The set of authoring tools developed for the project include tools to create several kinds of smart learning content, tools to create adaptive courses that use this content, tools to manage users and groups, as well as the portal to access different tools. Details are shown as follows.

==== Content Authoring Tools ====
{|
|valign="top" | [[Image:ExampleAuthoringModify.jpg|thumb|left|'''100'''|Annotated Example Authoring]]
|valign="top" | This set of tools provide the interface for teachers to create or modify problems or annotated examples, index problems or examples with concepts, arranged problems or examples under topics for Mastery Grids, etc. It supports multiple domains including Java and Python. Currently, for authoring problems, we developed QuizJET, QuizPET Authoring Systems; for authoring annotated examples, we developed Example Authoring System.

* [[Content Authoring Tools|More about Content Authoring Tools]]
* [[QuizJET Authoring System|More about QuizJET Authoring]]
* [[QuizPET Authoring System|More about QuizPET Authoring]]
* [[Example Authoring System|More about Example Authoring]]

|}

==== Course Authoring Tool ====
{|
|valign="top" | [[Image:CourseAuthoringCourseList.jpg|thumb|left|'''100'''|Course Authoring]]
|valign="top" | Course authoring tool provides the interface for teachers to create courses for Mastery Grids. It is an intuitive, highly integrated application for teaching material aggregation and editing. It allows the user to form the structure of a course, browse and select the activities from the resource pool and fill in the structure with appropriate examples and quizzes. The architecture supporting course authoring tool has a good support on multiple resource repositories, which is extremely convenient for future extending. [[Course Authoring Tool|==> more]]
|}
==== Group Authoring Tool ====
{|
|valign="top" | [[Image:GroupAuthoring.jpg|thumb|left|'''100'''|Course Authoring]]
|valign="top" | This tool provides the interface for teachers to define groups of students who can access the course. It includes functionalities such as search/select/create groups, view/modify user information, add/remove user to/from groups, search user, connect/disconnect group to/from existing applications, assign course to the group. [[Group Authoring Tool|==> more]]
|}
==== Authoring Tool Portal ====
{|
|valign="top" | [[Image:AuthoringPortal.jpg|thumb|left|'''100'''|Authoring Portal]]
|valign="top" | This is the central portal for providing access to the course authoring and group authoring tools. [[Authoring Tool Portal|==> more]]
|}

== Open Source ==
Software sources and documentations are in GitHub [https://github.com/PAWSLabUniversityOfPittsburgh PAWSLabUniversityOfPittsburgh Organization].
* The Mastery Grids Interface, back-end Aggregate and documentation can be found [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids here].
* User model services can be found in [https://github.com/PAWSLabUniversityOfPittsburgh/AggregateUMServices here].
* Annotated Examples Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/annotated-examples here]
* QuizJet Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/quizjet here]
* QuizPet Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/quizpet here]
* Videos User Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/educvideos here]

== Publications ==
* Gonzalez-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2013) FAST: Feature-Aware Student Knowledge Tracing. In: Proceedings of NIPS 2013 Workshop on Data Driven Education, Lake Tahoe, NV, December 10, 2013, ([http://d-scholarship.pitt.edu/20353/ paper])
* Hosseini, R. and Brusilovsky, P. (2013) JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems. In: Proceedings of The First Workshop on AI-supported Education for Computer Science (AIEDCS) at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN, USA, July 13, 2013, pp. 60-63. ([https://d-scholarship.pitt.edu/secure/26270/1/AIED2013-workshop-camera_ready_version.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/java-parser-a-fine-grained-indexing-tool-and-its-application presentation])
* Hosseini, R., Brusilovsky, P., and Guerra, J. (2013) Knowledge Maximizer: Concept-based Adaptive Problem Sequencing for Exam Preparation. In: Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013), Memphis, USA, pp. 848-851. ([https://d-scholarship.pitt.edu/secure/26271/4/AIED2013-camera-ready-Knowledge_maximizer_.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/aied-2013 poster])
* Brusilovsky, P., Baishya, D., Hosseini, R., Guerra, J., and Liang, M. (2013) KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model. In: Proceedings of 2013 IEEE 13th International Conference on Advanced Learning Technologies, Beijing, China, July 15-18, 2013, pp. 275-279. ([http://dx.doi.org/10.1109/ICALT.2013.86 paper]) ([http://www.slideshare.net/RoyaHosseini1/kowledge-zoom-michelle-48735584 presentation])
* Brusilovsky, P. (2014) Addictive Links: Engaging Students through Adaptive Navigation Support and Open Social Student Modeling (Keynote talk). In: Proceedings of WWW 2014 Workshop on Web-based Education Technologies, Seoul, Korea, April 8, 2014. ([http://www.slideshare.net/pbrusilovsky/addictive-links-keynote-talk-at-www-2014-workshop presentation])
* Huang, Y., Xu, Y., and Brusilovsky, P. (2014) Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models. In: V. Dimitrova, et al. (eds.) Proceedings of 22nd Conference on User Modeling, Adaptation and Personalization (UMAP 2014), Aalborg, Denmark, July 7-11, 2014, Springer Verlag, pp. 338-349. ([http://www.slideshare.net/pbrusilovsky/umap-v1 presentation][http://link.springer.com/chapter/10.1007%2F978-3-319-08786-3_30 paper])
* Hosseini, R. and Brusilovsky, P. (2014) Example-Based Problem Solving Support Using concept Analysis of Programming Content. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 683-685. ([https://d-scholarship.pitt.edu/secure/26268/1/CameraReady_ITS2014_paper.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/presentation-48735557 presentation])
* Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Exploring Problem Solving Paths in a Java Programming Course. In: Proceedings of Psychology of Programming Interest Group Annual Conference, PPIG 2014, Brighton, UK, June 25-27, 2014, pp. 65-76. ([https://d-scholarship.pitt.edu/secure/26272/1/PPIG_2014_camera_ready.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/ppig2014-problem-solvingpaths presentation])
* Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2014) General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 84-91. (First two authors contributed equally. Nominated for Best Paper Award) ([http://www.slideshare.net/huangyun/fast-presentation-48711687 presentation][http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf paper][http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial tutorial] [http://ml-smores.github.io/fast/ code])
* Khajah, M. M., Huang, Y., González-Brenes, J. P., Mozer, M. C., and Brusilovsky, P. (2014) Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. In: I. Cantador, M. Chi, R. Farzan and R. Jäschke (eds.) Proceedings of Workshop on Personalization Approaches in Learning Environments (PALE 2014) at the 22th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Aalborg, Denmark, July 11, 2014, CEUR, pp. 7-12. (First three authors contributed equally) ([http://www.slideshare.net/huangyun/pale-public-slideshare presentation][http://ceur-ws.org/Vol-1181/pale2014_paper_01.pdf paper]).
* Yudelson, M., Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Investigating Automated Student Modeling in a Java MOOC. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 261-264. ([https://d-scholarship.pitt.edu/secure/26273/1/EDM2014YudelsonHVB_camready.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/edm2014-investigating-automated-student-modeling-in-a-java-mooc presentation])
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Parameterized Exercises in Java Programming: Using Knowledge Structure for Performance Prediction. In: Proceedings of The second Workshop on AI-supported Education for Computer Science (AIEDCS) at 12th International Conference on Intelligent Tutoring Systems ITS 2014, Honolulu, Hawaii, June 6 2014. ([http://d-scholarship.pitt.edu/21915/ paper])([http://www.slideshare.net/chagh/parameterized-exercises-in-java-programming-using-knowledge-structure-for-performance-prediction presentation])
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Predicting Student Performance in Solving Parameterized Exercises. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 496-503, ([http://d-scholarship.pitt.edu/21916/ paper]) ([http://www.slideshare.net/chagh/its14-pitttemplate presentation])
* Guerra, J., Sahebi, S., Lin, Y.-R., and Brusilovsky, P. (2014) The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 153-160 ([http://www.slideshare.net/huangyun/guerra-the-problemsolvinggenome presentation][http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/153_EDM-2014-Full.pdf paper])
* Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014 (Best paper award). ([http://link.springer.com/chapter/10.1007%2F978-3-319-11200-8_18#page-1 paper]) ([http://www.slideshare.net/pbrusilovsky/ectel2014-mg presentation])
* Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) Increasing Adoption of Smart Learning Content for Computer Science Education. In: Proceedings of Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57. ([http://dx.doi.org/10.1145/2713609.2713611 paper])
* Gonzalez-Brenes, J. P., Huang, Y. (2014) The White Method: Towards Automatic Evaluation Metrics for Adaptive Tutoring Systems. In: Proceedings of NIPS 2014 Workshop on Human Propelled Machine Learning, Montreal, Canada, December 13, 2014 ([http://d-scholarship.pitt.edu/26061/ paper])
* Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2015) The FAST toolkit for Unsupervised Learning of HMMs with Features. In: The Machine Learning Open Source Software workshop at the 32nd International Conference on Machine Learning (ICML-MLOSS 2015), Lille, France July 10, 2015. ([http://d-scholarship.pitt.edu/26043/ paper][http://mloss.org/software/view/609/ code])
* Huang, Y., González-Brenes, J. P., Kumar, R., Brusilovsky, P. (2015) A Framework for Multifaceted Evaluation of Student Models. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, pp. 203-210. ([http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_164.pdf paper]) ([http://www.slideshare.net/huangyun/2015edm-a-framework-for-multifaceted-evaluation-of-student-models-polygon presentation])
* Huang, Y., González-Brenes, J. P., Brusilovsky, P. (2015) Challenges of Using Observational Data to Determine the Importance of Example Usage. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, pp. 633-637. ([http://d-scholarship.pitt.edu/26056/ paper])
* Gonzalez-Brenes, J. P., Huang, Y. (2015) Your model is predictive— but is it useful? Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, pp. 187-194. ([https://www.researchgate.net/publication/280805929_The_Value_of_Social_Comparing_Open_Student_Modeling_and_Open_Social_Student_Modeling paper] [http://www.slideshare.net/pbrusilovsky/umap2015-mg presentation])
* Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55 ([http://d-scholarship.pitt.edu/26046/ paper] [http://www.slideshare.net/huangyun/2015-edm-leopard-for-adaptive-tutoring-evaluation presentation])
* Guerra, J., Huang, Y., Hosseini, R., & Brusilovsky, P. (2015, June). Graph Analysis of Student Model Networks. In Proceedings of the Second International Workshop on Graph-Based Educational Data Mining (GEDM 2015). CEUR-WS. ([https://d-scholarship.pitt.edu/secure/25933/1/graph_analysis.pdf paper]) ([http://www.slideshare.net/mallium/graph-analysis-of-student-model-networks presentation])
* Guerra, J., Huang, Y., Hosseini, R., & Brusilovsky, P. Exploring the Effects of Open Social Student Model Beyond Social Comparison. In ISLG 2015 Fourth Workshop on Intelligent Support for Learning in Groups (p. 19). ([https://d-scholarship.pitt.edu/secure/25931/1/islg_pap4.pdf paper]) ([http://www.slideshare.net/mallium/exploring-the-effects-of-open-social-student-model-beyond-social-comparison poster])
* Hosseini, R., Hsiao, I.-H., Guerra, J., Brusilovsky, P. (2015) Off the Beaten Path: The Impact of Adaptive Content Sequencing on Student Navigation in an Open Social Student Modeling Interface. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, pp. 624-628. ([https://d-scholarship.pitt.edu/secure/25938/1/paper_183.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/aied-2015-poster-off-the-beaten-path-the-impact-of-adaptive-content-sequencing-on-student-navigation-in-an-open-social-student-modeling-interface poster])
* Hosseini, R., Hsiao, I.-H., Guerra, J., Brusilovsky, P. (2015) What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling. Proceedings of 10th European Conference on Technology Enhanced Learning (EC-TEL 2015), Toledo, Spain, September 15-18, 2015, pp. 155-168. ([https://d-scholarship.pitt.edu/secure/26266/1/camera_ready.pdf paper])([http://www.slideshare.net/RoyaHosseini1/ectel-2015 presentation]).
* Somyürek, S. & Brusilovsky, P. (2015). Impact of Open Social Student Modeling on Self-Assessment of Performance. Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2009 (E-Learn 2015). Kona, Hawaii, United States, October 19-22, 2015
* Hosseini, R., Sirkiä, T., Guerra, J., Brusilovsky, P., Malmi, L. (2016) Animated Examples as Practice Content in a Java Programming Course. Proceedings of the 47th ACM technical symposium on Computer Science Education (SIGCSE), Memphis, Tennessee, March 2-5, 2016. ([https://d-scholarship.pitt.edu/secure/27083/1/sigcse2016.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/sigcse-2016 presentation])

Course Authoring Tool

2016-04-04T19:57:16Z

Hnvasa: /* Resources */

Course authoring tool is an intuitive, highly integrated application for teaching material aggregation and editing. It allows the user to form the structure of a course, browse and select the activities from the resource pool and fill in the structure with appropriate examples and quizzes. The architecture supporting course authoring tool has a good support on multiple resource repositories, which is extremely convenient for future extending.

It contains software components as follows:
* Front-end user-system interaction interface, the tool’s interface
* Back-end communication APIs
* Back-end aggregates
* Back-end content providing applications (content brokering)

The interface of the course authoring tool uses color cues and drag interaction methods to ease the process of editing.
Communication APIs provides the program interface for the JavaScript to use AJAX, which will allow the user to perform all the operations in one webpage.
The content providing applications are the main access for course authoring tool to list out the available content for the user.

Here is the interface for Course List Panel:

[[Image:CourseAuthoringCourseList.jpg|750x1400px]]

Here is the interface for Course Info Panel:

[[Image:CourseAuthoringCourseInfo.jpg|750x1400px]]

== Resources ==
* Source codes are available in GitHub in [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids/tree/master/course-authoring here].
* [[Media:CourseAuthoringManualV3.pdf|Course Authoring Tool Manual]]

Adaptive Navigation Support and Open Social Learner Modeling for PAL

2016-04-04T19:52:03Z

Hnvasa:

== Overview ==

The goal of this project is to leverage the power of [[open social learner modeling]] and [[adaptive navigation support]] in the context of the envisioned Personalized Assistant for Learning (PAL). The project is supported by the [http://adlnet.gov Advanced Distributed Learning Initiative] contract W911QY13C0032. This is a joint project with [http://cs.aalto.fi/en/research/ Learning + Technology] research group at Aalto University. The group at Aalto University aims at producing smart learning content on Python programming, of which details can be accessed [http://acos.cs.hut.fi/ here] or from specific sections on this page.

The project focuses on both exploration and implementation of adaptive navigation support and open social learner modeling and pursues three directions of work:

* Exploring open social learner modeling interface for diverse learning content
* Enhancing algorithms for personalized guidance using knowledge-based and social approaches
* Developing architectural solutions and authoring tools to support open social learner modeling

== Open Social Learner Model Interface: Mastery Grids ==

{|
|valign="top" | [[Image:Mg_1.png|thumb|left|'''100'''|Mastery Grids Interface]]
|valign="top" | Our latest implementation of Open Social Learner Modeling (OSLM) is [[Mastery Grids Interface]]. Mastery Grids is both an innovative Open Social Learner Model Interface and an adaptive E-learning platform with integrated functionalities enabling multi-facet social comparison, open learner modeling, and adaptive navigation support to access multiple kinds of smart learning content. Mastery Grids is supported by adaptive social learning framework [[Aggregate]]. This framework supports several kinds of open student modeling, social comparison, and recommendation. In detail, Mastery Grids presents and compares user learning progress and knowledge level using colored grids, tracks user activities with learning content, and provides flexible user-centered navigation across different content levels (e.g. topic, question) and different content types (e.g. problem, example). Our past research shows that open student modeling and social comparison effectively increases students’ performance, motivation, engagement and retention.

* [[Mastery Grids Interface|More about Mastery Grids interface]]
* [http://adapt2.sis.pitt.edu/um-vis-adl/index.html?usr=adl01&grp=ADL&sid=test&cid=13&data-top-n-grp=5&def-val-rep-lvl-id=p&def-val-res-id=AVG&ui-tbar-rep-lvl-vis=0&ui-tbar-topic-size-vis=0 An interactive demo of Mastery Grids interface]
|}

== Architecture: Aggregate==
{|
|valign="top" | [[Image:arch_v2.png|thumb|left|'''100'''|Aggregate Architecture]]
|valign="top" | We developed an adaptive social learning architecture [[Aggregate]] to support Mastery Grids interface. [[Aggregate]] is an extension of our original [[ADAPT2]] architecture. On the top of [[ADAPT2]] , [[Aggregate]] architecture supports several kinds of open student modeling, social comparison, content brokering, and recommendation services. The architecture fulfills a major objective, portability, which is the ability to be integrated to other systems with little set up and modification. The architecture is modular and includes different software components.

* [[ADAPT2| More about ADAPT2]]
* [[Aggregate| More about Aggregate]]
|}

== Learner Model ==
{|
|valign="top" | [[Image:CUMULATE.evidence propagation.png|thumb|left|'''100'''|CUMULATE]]
|valign="center" | We have developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. CUMULATE allows flexible learner models to infer learner knowledge. Mastery Grids's architecture is supported by CUMULATE and thus it also supports flexible learner models. We have proposed and implemented different learner models over past years, including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]] which is the main one currently deployed in our systems, and [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance. The explanation of the communication between the interface and learner model can be found in [[Aggregate]].

* [[CUMULATE|More about CUMULATE]]
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]
|}

== Personalization ==
{|
|valign="top" | [[Image:starRecommendation.png|thumb|left|'''100'''|Personalization]]
|valign="top" | There are multiple personalization approaches, that are developed and researched in our system. In the form of recommendations, we have various methods in different levels for recommending learning material to students. Two major approached for recommending resources are reactive and proactive recommendations. In the reactive approach, the recommender system activates in reaction to the student's activity, e.g. if the student fails in solving a quiz, the reactive recommender system recommends related examples to this student to help her understand the skills required to solve that quiz. The pro-active recommender system, proactively suggests learning materials to the students.

|}

== Educational Data Mining ==
{|
|valign="top" | [[Image:TensorFactorization.png|thumb|left|'''100'''|Educational Data Mining]]
|valign="top" | In this part of the project, we aim to make sense of data from Mastery Grids system, including logs of student attempts. The goal in this part includes understanding students' learning patterns and its relationship with students' behavioral traits, predicting students' performance, modeling student knowledge, and discovering the content model. These tasks eventually help us in providing a better service to both instructors and students. [[Educational Data Mining|==> more]]

|}

== Smart Content ==
{|
|valign="top" | [[Image:AnimatedExamples.jpg|thumb|left|'''100'''|Smart Content (Animated Examples)]]
|valign="top" | Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. We have developed different content applications (e.g., [[QuizJET]], [[QuizPET]], [[SQLKnoT]], [[WebEx]]) and authoring tools (e.g., [[Content Authoring Tools]], [[Course Authoring Tool]], [[Group Authoring Tool]], [[Authoring Tool Portal]]) for accessing and authoring such contents. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain problems (quizzes), parson problems, annotated examples, and animated examples collected from experienced course teachers, textbooks or domain experts. [[Smart Content|==> more]]
|}

== Authoring Tools ==
The set of authoring tools developed for the project include tools to create several kinds of smart learning content, tools to create adaptive courses that use this content, tools to manage users and groups, as well as the portal to access different tools. Details are shown as follows.

==== Content Authoring Tools ====
{|
|valign="top" | [[Image:ExampleAuthoringModify.jpg|thumb|left|'''100'''|Annotated Example Authoring]]
|valign="top" | This set of tools provide the interface for teachers to create or modify problems or annotated examples, index problems or examples with concepts, arranged problems or examples under topics for Mastery Grids, etc. It supports multiple domains including Java and Python. Currently, for authoring problems, we developed QuizJET, QuizPET Authoring Systems; for authoring annotated examples, we developed Example Authoring System.

* [[Content Authoring Tools|More about Content Authoring Tools]]
* [[QuizJET Authoring System|More about QuizJET Authoring]]
* [[QuizPET Authoring System|More about QuizPET Authoring]]
* [[Example Authoring System|More about Example Authoring]]

|}

==== Course Authoring Tool ====
{|
|valign="top" | [[Image:CourseAuthoringCourseList.jpg|thumb|left|'''100'''|Course Authoring]]
|valign="top" | Course authoring tool provides the interface for teachers to create courses for Mastery Grids. It is an intuitive, highly integrated application for teaching material aggregation and editing. It allows the user to form the structure of a course, browse and select the activities from the resource pool and fill in the structure with appropriate examples and quizzes. The architecture supporting course authoring tool has a good support on multiple resource repositories, which is extremely convenient for future extending. [[Course Authoring Tool|==> more]]
|}
==== Group Authoring Tool ====
{|
|valign="top" | [[Image:GroupAuthoring.jpg|thumb|left|'''100'''|Course Authoring]]
|valign="top" | This tool provides the interface for teachers to define groups of students who can access the course. It includes functionalities such as search/select/create groups, view/modify user information, add/remove user to/from groups, search user, connect/disconnect group to/from existing applications, assign course to the group. [[Group Authoring Tool|==> more]]
|}
==== Authoring Tool Portal ====
{|
|valign="top" | [[Image:AuthoringPortal.jpg|thumb|left|'''100'''|Authoring Portal]]
|valign="top" | This is the central portal for providing access to the course authoring and group authoring tools. [[Authoring Tool Portal|==> more]]
|}

== Open Source ==
Software sources and documentation is in GitHub. The Mastery Grids Interface, backend Aggregate and documentation can be found [https://github.com/PAWSLabUniversityOfPittsburgh/MasteryGrids here]. User model services can be found in [https://github.com/PAWSLabUniversityOfPittsburgh/AggregateUMServices here].

== Publications ==
* Gonzalez-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2013) FAST: Feature-Aware Student Knowledge Tracing. In: Proceedings of NIPS 2013 Workshop on Data Driven Education, Lake Tahoe, NV, December 10, 2013, ([http://d-scholarship.pitt.edu/20353/ paper])
* Hosseini, R. and Brusilovsky, P. (2013) JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems. In: Proceedings of The First Workshop on AI-supported Education for Computer Science (AIEDCS) at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN, USA, July 13, 2013, pp. 60-63. ([https://d-scholarship.pitt.edu/secure/26270/1/AIED2013-workshop-camera_ready_version.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/java-parser-a-fine-grained-indexing-tool-and-its-application presentation])
* Hosseini, R., Brusilovsky, P., and Guerra, J. (2013) Knowledge Maximizer: Concept-based Adaptive Problem Sequencing for Exam Preparation. In: Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013), Memphis, USA, pp. 848-851. ([https://d-scholarship.pitt.edu/secure/26271/4/AIED2013-camera-ready-Knowledge_maximizer_.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/aied-2013 poster])
* Brusilovsky, P., Baishya, D., Hosseini, R., Guerra, J., and Liang, M. (2013) KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model. In: Proceedings of 2013 IEEE 13th International Conference on Advanced Learning Technologies, Beijing, China, July 15-18, 2013, pp. 275-279. ([http://dx.doi.org/10.1109/ICALT.2013.86 paper]) ([http://www.slideshare.net/RoyaHosseini1/kowledge-zoom-michelle-48735584 presentation])
* Brusilovsky, P. (2014) Addictive Links: Engaging Students through Adaptive Navigation Support and Open Social Student Modeling (Keynote talk). In: Proceedings of WWW 2014 Workshop on Web-based Education Technologies, Seoul, Korea, April 8, 2014. ([http://www.slideshare.net/pbrusilovsky/addictive-links-keynote-talk-at-www-2014-workshop presentation])
* Huang, Y., Xu, Y., and Brusilovsky, P. (2014) Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models. In: V. Dimitrova, et al. (eds.) Proceedings of 22nd Conference on User Modeling, Adaptation and Personalization (UMAP 2014), Aalborg, Denmark, July 7-11, 2014, Springer Verlag, pp. 338-349. ([http://www.slideshare.net/pbrusilovsky/umap-v1 presentation][http://link.springer.com/chapter/10.1007%2F978-3-319-08786-3_30 paper])
* Hosseini, R. and Brusilovsky, P. (2014) Example-Based Problem Solving Support Using concept Analysis of Programming Content. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 683-685. ([https://d-scholarship.pitt.edu/secure/26268/1/CameraReady_ITS2014_paper.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/presentation-48735557 presentation])
* Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Exploring Problem Solving Paths in a Java Programming Course. In: Proceedings of Psychology of Programming Interest Group Annual Conference, PPIG 2014, Brighton, UK, June 25-27, 2014, pp. 65-76. ([https://d-scholarship.pitt.edu/secure/26272/1/PPIG_2014_camera_ready.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/ppig2014-problem-solvingpaths presentation])
* Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2014) General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 84-91. (First two authors contributed equally. Nominated for Best Paper Award) ([http://www.slideshare.net/huangyun/fast-presentation-48711687 presentation][http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf paper][http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial tutorial] [http://ml-smores.github.io/fast/ code])
* Khajah, M. M., Huang, Y., González-Brenes, J. P., Mozer, M. C., and Brusilovsky, P. (2014) Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. In: I. Cantador, M. Chi, R. Farzan and R. Jäschke (eds.) Proceedings of Workshop on Personalization Approaches in Learning Environments (PALE 2014) at the 22th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Aalborg, Denmark, July 11, 2014, CEUR, pp. 7-12. (First three authors contributed equally) ([http://www.slideshare.net/huangyun/pale-public-slideshare presentation][http://ceur-ws.org/Vol-1181/pale2014_paper_01.pdf paper]).
* Yudelson, M., Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Investigating Automated Student Modeling in a Java MOOC. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 261-264. ([https://d-scholarship.pitt.edu/secure/26273/1/EDM2014YudelsonHVB_camready.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/edm2014-investigating-automated-student-modeling-in-a-java-mooc presentation])
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Parameterized Exercises in Java Programming: Using Knowledge Structure for Performance Prediction. In: Proceedings of The second Workshop on AI-supported Education for Computer Science (AIEDCS) at 12th International Conference on Intelligent Tutoring Systems ITS 2014, Honolulu, Hawaii, June 6 2014. ([http://d-scholarship.pitt.edu/21915/ paper])([http://www.slideshare.net/chagh/parameterized-exercises-in-java-programming-using-knowledge-structure-for-performance-prediction presentation])
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Predicting Student Performance in Solving Parameterized Exercises. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 496-503, ([http://d-scholarship.pitt.edu/21916/ paper]) ([http://www.slideshare.net/chagh/its14-pitttemplate presentation])
* Guerra, J., Sahebi, S., Lin, Y.-R., and Brusilovsky, P. (2014) The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 153-160 ([http://www.slideshare.net/huangyun/guerra-the-problemsolvinggenome presentation][http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/153_EDM-2014-Full.pdf paper])
* Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014 (Best paper award). ([http://link.springer.com/chapter/10.1007%2F978-3-319-11200-8_18#page-1 paper]) ([http://www.slideshare.net/pbrusilovsky/ectel2014-mg presentation])
* Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) Increasing Adoption of Smart Learning Content for Computer Science Education. In: Proceedings of Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57. ([http://dx.doi.org/10.1145/2713609.2713611 paper])
* Gonzalez-Brenes, J. P., Huang, Y. (2014) The White Method: Towards Automatic Evaluation Metrics for Adaptive Tutoring Systems. In: Proceedings of NIPS 2014 Workshop on Human Propelled Machine Learning, Montreal, Canada, December 13, 2014 ([http://d-scholarship.pitt.edu/26061/ paper])
* Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2015) The FAST toolkit for Unsupervised Learning of HMMs with Features. In: The Machine Learning Open Source Software workshop at the 32nd International Conference on Machine Learning (ICML-MLOSS 2015), Lille, France July 10, 2015. ([http://d-scholarship.pitt.edu/26043/ paper][http://mloss.org/software/view/609/ code])
* Huang, Y., González-Brenes, J. P., Kumar, R., Brusilovsky, P. (2015) A Framework for Multifaceted Evaluation of Student Models. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, pp. 203-210. ([http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_164.pdf paper]) ([http://www.slideshare.net/huangyun/2015edm-a-framework-for-multifaceted-evaluation-of-student-models-polygon presentation])
* Huang, Y., González-Brenes, J. P., Brusilovsky, P. (2015) Challenges of Using Observational Data to Determine the Importance of Example Usage. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, pp. 633-637. ([http://d-scholarship.pitt.edu/26056/ paper])
* Gonzalez-Brenes, J. P., Huang, Y. (2015) Your model is predictive— but is it useful? Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, pp. 187-194. ([https://www.researchgate.net/publication/280805929_The_Value_of_Social_Comparing_Open_Student_Modeling_and_Open_Social_Student_Modeling paper] [http://www.slideshare.net/pbrusilovsky/umap2015-mg presentation])
* Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55 ([http://d-scholarship.pitt.edu/26046/ paper] [http://www.slideshare.net/huangyun/2015-edm-leopard-for-adaptive-tutoring-evaluation presentation])
* Guerra, J., Huang, Y., Hosseini, R., & Brusilovsky, P. (2015, June). Graph Analysis of Student Model Networks. In Proceedings of the Second International Workshop on Graph-Based Educational Data Mining (GEDM 2015). CEUR-WS. ([https://d-scholarship.pitt.edu/secure/25933/1/graph_analysis.pdf paper]) ([http://www.slideshare.net/mallium/graph-analysis-of-student-model-networks presentation])
* Guerra, J., Huang, Y., Hosseini, R., & Brusilovsky, P. Exploring the Effects of Open Social Student Model Beyond Social Comparison. In ISLG 2015 Fourth Workshop on Intelligent Support for Learning in Groups (p. 19). ([https://d-scholarship.pitt.edu/secure/25931/1/islg_pap4.pdf paper]) ([http://www.slideshare.net/mallium/exploring-the-effects-of-open-social-student-model-beyond-social-comparison poster])
* Hosseini, R., Hsiao, I.-H., Guerra, J., Brusilovsky, P. (2015) Off the Beaten Path: The Impact of Adaptive Content Sequencing on Student Navigation in an Open Social Student Modeling Interface. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, pp. 624-628. ([https://d-scholarship.pitt.edu/secure/25938/1/paper_183.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/aied-2015-poster-off-the-beaten-path-the-impact-of-adaptive-content-sequencing-on-student-navigation-in-an-open-social-student-modeling-interface poster])
* Hosseini, R., Hsiao, I.-H., Guerra, J., Brusilovsky, P. (2015) What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling. Proceedings of 10th European Conference on Technology Enhanced Learning (EC-TEL 2015), Toledo, Spain, September 15-18, 2015, pp. 155-168. ([https://d-scholarship.pitt.edu/secure/26266/1/camera_ready.pdf paper])([http://www.slideshare.net/RoyaHosseini1/ectel-2015 presentation]).
* Somyürek, S. & Brusilovsky, P. (2015). Impact of Open Social Student Modeling on Self-Assessment of Performance. Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2009 (E-Learn 2015). Kona, Hawaii, United States, October 19-22, 2015
* Hosseini, R., Sirkiä, T., Guerra, J., Brusilovsky, P., Malmi, L. (2016) Animated Examples as Practice Content in a Java Programming Course. Proceedings of the 47th ACM technical symposium on Computer Science Education (SIGCSE), Memphis, Tennessee, March 2-5, 2016. ([https://d-scholarship.pitt.edu/secure/27083/1/sigcse2016.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/sigcse-2016 presentation])

JSWebEx

2016-04-04T19:49:41Z

Hnvasa:

JSWebEx is a Node.js (Javascript) application currently used as a web service to fetch the content from the database and display it in the web browser. It is also used for the User Interface of the Annotated examples. Currently JSWebEx supports domains like Java, Python, SQL, C, C++ and VB.

[[Image:jswebex.png|650x800px]]

== Resources ==
Source codes are available in GitHub in [https://github.com/PAWSLabUniversityOfPittsburgh/annotated-examples/tree/master/jswebex here].

[[Media:jswebex_documetation.pdf|JSWebEx Manual]]

File:Jswebex documetation.pdf

2016-04-04T19:48:20Z

Hnvasa: documentation for jswebex

documentation for jswebex

JSWebEx

2016-04-04T19:46:23Z

Hnvasa:

JSWebEx

2016-04-04T19:44:50Z

Hnvasa: /* Resources */

Smart Content

2016-04-04T19:35:32Z

Hnvasa: /* Annotated Examples */

Smart Content

2016-04-04T19:32:18Z

Hnvasa: /* Annotated Examples */

File:Jswebex.png

2016-04-04T19:31:04Z

Hnvasa: new image

new image

Smart Content

2016-04-04T19:30:44Z

Hnvasa: /* Annotated Examples */

Mastery Grids Interface

2016-04-04T18:32:20Z

Hnvasa: /* Videos */

Mastery Grids is an open social learner modeling interface written in Javascript. The interface shows the student's progress or knowledge in topics or content types comparing with other learners, and provides navigation support to access suitable educational content. 

== Open Learner Model ==
Mastery Grids is our latest implementation of open learner modeling built based on our past successful systems (e.g., InterBook, QuizGuide, NavEx, QuizMap, Progressor). Combining open learner model with social comparison, i.e., open social learner model, Mastery Grids not only enables a student to be aware of the strength and weakness among his/her own knowledge modules, but also stimulates students to work harder. Our open social learner model encourages students to catch up with other students (observing average class progress or advanced students’ progress) and follow the potential good learning path of the peers.

== Knowledge and Progress Visualization ==
Mastery Grids presents and compares user learning progress and knowledge level (mastery) by colored grids from four perspectives as shown in the following figure. The four perspectives show the current student’s progress (“Me” or my progress), a comparison of performance of current student versus other student in the group (“Me vs group” or comparison grid), performance of the students in the group (“Group” or group grid), and performance of all of the students in the class.

[[Image:MG1.jpg|1000x1500px]]

== Social Comparison ==
Social comparison is implemented in two ways: 1) Me vs group, which compares an individual learner’s progress or knowledge level with the entire group of students, and 2) Me vs other students, which compares an an individual learner’s progress or knowledge level with each of other students. Such comparisons can be further conducted by resource types (learning materials) as shown in following figure.

[[Image:MG2.jpg|1000x1500px]]

== Videos ==
* Watch a video about Mastery Grids interface [https://www.youtube.com/watch?v=76YLR2VY2QE here].
* Watch a video about Mastery Grids with adaptive content sequencing [https://www.youtube.com/watch?v=Kak8F2y5GkU here].

== Links ==
* Try an interactive demo of Mastery Grids [http://adapt2.sis.pitt.edu/um-vis-adl/index.html?usr=adl01&grp=ADL&sid=test&cid=13&data-top-n-grp=5&def-val-rep-lvl-id=p&def-val-res-id=AVG&ui-tbar-rep-lvl-vis=0&ui-tbar-topic-size-vis=0 here]
* Read a slide presentation about Mastery Grids [http://www.slideshare.net/pbrusilovsky/ectel2014-mg here].
* Download the source code [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids here].

== Publications ==

* Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014 (Best paper award). ([http://link.springer.com/chapter/10.1007%2F978-3-319-11200-8_18#page-1 paper]) ([http://www.slideshare.net/pbrusilovsky/ectel2014-mg presentation])
* Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55 ([http://d-scholarship.pitt.edu/26046/ paper] [http://www.slideshare.net/huangyun/2015-edm-leopard-for-adaptive-tutoring-evaluation presentation])

Mastery Grids Interface

2016-04-04T17:39:40Z

Hnvasa: /* Videos */

Mastery Grids is an open social learner modeling interface written in Javascript. The interface shows the student's progress or knowledge in topics or content types comparing with other learners, and provides navigation support to access suitable educational content. 

== Open Learner Model ==
Mastery Grids is our latest implementation of open learner modeling built based on our past successful systems (e.g., InterBook, QuizGuide, NavEx, QuizMap, Progressor). Combining open learner model with social comparison, i.e., open social learner model, Mastery Grids not only enables a student to be aware of the strength and weakness among his/her own knowledge modules, but also stimulates students to work harder. Our open social learner model encourages students to catch up with other students (observing average class progress or advanced students’ progress) and follow the potential good learning path of the peers.

== Knowledge and Progress Visualization ==
Mastery Grids presents and compares user learning progress and knowledge level (mastery) by colored grids from four perspectives as shown in the following figure. The four perspectives show the current student’s progress (“Me” or my progress), a comparison of performance of current student versus other student in the group (“Me vs group” or comparison grid), performance of the students in the group (“Group” or group grid), and performance of all of the students in the class.

[[Image:MG1.jpg|1000x1500px]]

== Social Comparison ==
Social comparison is implemented in two ways: 1) Me vs group, which compares an individual learner’s progress or knowledge level with the entire group of students, and 2) Me vs other students, which compares an an individual learner’s progress or knowledge level with each of other students. Such comparisons can be further conducted by resource types (learning materials) as shown in following figure.

[[Image:MG2.jpg|1000x1500px]]

== Videos ==
* Watch a video about Mastery Grids with adaptive content sequencing [https://www.youtube.com/watch?v=Kak8F2y5GkU here].

== Links ==
* Try an interactive demo of Mastery Grids [http://adapt2.sis.pitt.edu/um-vis-adl/index.html?usr=adl01&grp=ADL&sid=test&cid=13&data-top-n-grp=5&def-val-rep-lvl-id=p&def-val-res-id=AVG&ui-tbar-rep-lvl-vis=0&ui-tbar-topic-size-vis=0 here]
* Read a slide presentation about Mastery Grids [http://www.slideshare.net/pbrusilovsky/ectel2014-mg here].
* Download the source code [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids here].

== Publications ==

* Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014 (Best paper award). ([http://link.springer.com/chapter/10.1007%2F978-3-319-11200-8_18#page-1 paper]) ([http://www.slideshare.net/pbrusilovsky/ectel2014-mg presentation])
* Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55 ([http://d-scholarship.pitt.edu/26046/ paper] [http://www.slideshare.net/huangyun/2015-edm-leopard-for-adaptive-tutoring-evaluation presentation])

Mastery Grids Interface

2016-04-04T17:39:14Z

Hnvasa:

Mastery Grids is an open social learner modeling interface written in Javascript. The interface shows the student's progress or knowledge in topics or content types comparing with other learners, and provides navigation support to access suitable educational content. 

== Open Learner Model ==
Mastery Grids is our latest implementation of open learner modeling built based on our past successful systems (e.g., InterBook, QuizGuide, NavEx, QuizMap, Progressor). Combining open learner model with social comparison, i.e., open social learner model, Mastery Grids not only enables a student to be aware of the strength and weakness among his/her own knowledge modules, but also stimulates students to work harder. Our open social learner model encourages students to catch up with other students (observing average class progress or advanced students’ progress) and follow the potential good learning path of the peers.

== Knowledge and Progress Visualization ==
Mastery Grids presents and compares user learning progress and knowledge level (mastery) by colored grids from four perspectives as shown in the following figure. The four perspectives show the current student’s progress (“Me” or my progress), a comparison of performance of current student versus other student in the group (“Me vs group” or comparison grid), performance of the students in the group (“Group” or group grid), and performance of all of the students in the class.

[[Image:MG1.jpg|1000x1500px]]

== Social Comparison ==
Social comparison is implemented in two ways: 1) Me vs group, which compares an individual learner’s progress or knowledge level with the entire group of students, and 2) Me vs other students, which compares an an individual learner’s progress or knowledge level with each of other students. Such comparisons can be further conducted by resource types (learning materials) as shown in following figure.

[[Image:MG2.jpg|1000x1500px]]

== Videos ==
* Watch a video about Mastery Grids with sequencing [https://www.youtube.com/watch?v=Kak8F2y5GkU here].

== Links ==
* Try an interactive demo of Mastery Grids [http://adapt2.sis.pitt.edu/um-vis-adl/index.html?usr=adl01&grp=ADL&sid=test&cid=13&data-top-n-grp=5&def-val-rep-lvl-id=p&def-val-res-id=AVG&ui-tbar-rep-lvl-vis=0&ui-tbar-topic-size-vis=0 here]
* Read a slide presentation about Mastery Grids [http://www.slideshare.net/pbrusilovsky/ectel2014-mg here].
* Download the source code [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids here].

== Publications ==

* Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014 (Best paper award). ([http://link.springer.com/chapter/10.1007%2F978-3-319-11200-8_18#page-1 paper]) ([http://www.slideshare.net/pbrusilovsky/ectel2014-mg presentation])
* Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55 ([http://d-scholarship.pitt.edu/26046/ paper] [http://www.slideshare.net/huangyun/2015-edm-leopard-for-adaptive-tutoring-evaluation presentation])