Learning Recommendation - Revision history

R.hosseini: /* Recommendations within an Example */

2016-04-05T14:04:29Z

‎Recommendations within an Example

R.hosseini: /* Recommendations in Examples */

2016-04-05T14:03:52Z

‎Recommendations in Examples

R.hosseini: /* ProActive Recommendation */

2016-04-05T13:30:06Z

‎ProActive Recommendation

R.hosseini: /* ReActive Recommendation */

2016-04-05T13:29:46Z

‎ReActive Recommendation

R.hosseini: /* ReActive Recommendation */

2016-04-05T13:21:45Z

‎ReActive Recommendation

R.hosseini: /* ReActive Recommendation */

2016-04-05T13:21:29Z

‎ReActive Recommendation

R.hosseini: /* ReActive Recommendation */

2016-04-05T13:21:15Z

‎ReActive Recommendation

R.hosseini: /* ReActive Recommendation */

2016-04-05T13:21:04Z

‎ReActive Recommendation

R.hosseini: /* ReActive Recommendation */

2016-04-05T13:13:14Z

‎ReActive Recommendation

R.hosseini: /* ReActive Recommendation */

2016-04-05T13:12:50Z

‎ReActive Recommendation

← Older revision		Revision as of 14:04, 5 April 2016
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−	== Recommendations ~~within an Example~~ ==	+	== Recommendations inside Examples ==
	The goal of this work was to extend WebEx with more advanced knowledge-based link annotation that could guide users to the most appropriate lines by showing how much knowledge about the concepts presented in each line the user already mastered. The new functionality was made feasible by two intelligent technologies: the ability to automatically identify programming concepts associated with each line of code using concept parser and dynamic student model that maintains the current level of student knowledge for each concept. Using the current level of knowledge for concepts associated with each line, we can calculate how much knowledge associated with each line is already known to the learner. We expected that visualizing this dynamically (i.e., displaying the amount of knowledge as a visual cue next to the line) could help users to select most important lines. We also wanted to directly recommend most important lines to explore to the user.		The goal of this work was to extend WebEx with more advanced knowledge-based link annotation that could guide users to the most appropriate lines by showing how much knowledge about the concepts presented in each line the user already mastered. The new functionality was made feasible by two intelligent technologies: the ability to automatically identify programming concepts associated with each line of code using concept parser and dynamic student model that maintains the current level of student knowledge for each concept. Using the current level of knowledge for concepts associated with each line, we can calculate how much knowledge associated with each line is already known to the learner. We expected that visualizing this dynamically (i.e., displaying the amount of knowledge as a visual cue next to the line) could help users to select most important lines. We also wanted to directly recommend most important lines to explore to the user.

← Older revision		Revision as of 14:03, 5 April 2016
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−	== Recommendations ~~in Examples~~ ==	+	== Recommendations within an Example ==
	The goal of this work was to extend WebEx with more advanced knowledge-based link annotation that could guide users to the most appropriate lines by showing how much knowledge about the concepts presented in each line the user already mastered. The new functionality was made feasible by two intelligent technologies: the ability to automatically identify programming concepts associated with each line of code using concept parser and dynamic student model that maintains the current level of student knowledge for each concept. Using the current level of knowledge for concepts associated with each line, we can calculate how much knowledge associated with each line is already known to the learner. We expected that visualizing this dynamically (i.e., displaying the amount of knowledge as a visual cue next to the line) could help users to select most important lines. We also wanted to directly recommend most important lines to explore to the user.		The goal of this work was to extend WebEx with more advanced knowledge-based link annotation that could guide users to the most appropriate lines by showing how much knowledge about the concepts presented in each line the user already mastered. The new functionality was made feasible by two intelligent technologies: the ability to automatically identify programming concepts associated with each line of code using concept parser and dynamic student model that maintains the current level of student knowledge for each concept. Using the current level of knowledge for concepts associated with each line, we can calculate how much knowledge associated with each line is already known to the learner. We expected that visualizing this dynamically (i.e., displaying the amount of knowledge as a visual cue next to the line) could help users to select most important lines. We also wanted to directly recommend most important lines to explore to the user.

@@ Line 5: / Line 5: @@
 Adaptive sequencing is a greedy sequencing approach aimed at maximizing each student’s level of knowledge and activities. The learning material are ranked by balancing the knowledge level of each student in the prerequisite concepts of the learning material with the knowledge that can be gained from the outcome concepts.
 The probabilistic outspread approach aims to select the learning material that increases the probability of the student solving all of the quizzes in the course. It uses FAST as its underlying probabilistic model of student's knowledge and performance.
 [[Image:StarRecommendation.png|325x650px]]

@@ Line 16: / Line 16: @@
-Example line recommendation:
+== Recommendations in Examples ==
-The following images show example line recommendation and learning resource recommendation in Mastery Grids.
+The goal of this work was to extend WebEx with more advanced knowledge-based link annotation that could guide users to the most appropriate lines by showing how much knowledge about the concepts presented in each line the user already mastered. The new functionality was made feasible by two intelligent technologies: the ability to automatically identify programming concepts associated with each line of code using concept parser and dynamic student model that maintains the current level of student knowledge for each concept. Using the current level of knowledge for concepts associated with each line, we can calculate how much knowledge associated with each line is already known to the learner. We expected that visualizing this dynamically (i.e., displaying the amount of knowledge as a visual cue next to the line) could help users to select most important lines. We also wanted to directly recommend most important lines to explore to the user.
 [[Image:ANS_example.jpg|600x500px]]

← Older revision		Revision as of 13:21, 5 April 2016
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	These recommendations occur only when student fails in solving a question correctly in Mastery Grids. The student is going to be suggested some examples that can help the student acquire the skills needed to solve the original question.		These recommendations occur only when student fails in solving a question correctly in Mastery Grids. The student is going to be suggested some examples that can help the student acquire the skills needed to solve the original question.

−	[[Image:Reactive_rec.jpg\|~~600x500px~~]]	+	[[Image:Reactive_rec.jpg\|700x600px]]

@@ Line 12: / Line 12: @@
 == ReActive Recommendation ==
 These recommendations occur only when student fails in solving a question correctly in Mastery Grids. The student is going to be suggested some examples that can help the student acquire the skills needed to solve the original question.
 [[Image:Reactive_rec.jpg|600x500px]]

@@ Line 17: / Line 17: @@
 Example line recommendation:
-[[Image:ANS_example.jpg|900px]]
+[[Image:ANS_example.jpg|600x500px]]
 == Publications ==