CSEdPad - Revision history

Peterb at 22:05, 29 March 2024

2024-03-29T22:05:20Z

Peterb: /* University of Pittsburgh Team */

2024-03-29T22:04:40Z

‎University of Pittsburgh Team

Peterb at 22:04, 29 March 2024

2024-03-29T22:04:12Z

Peterb: /* Publications */

2024-03-29T21:50:03Z

‎Publications

Peterb at 21:49, 29 March 2024

2024-03-29T21:49:22Z

Peterb at 21:40, 29 March 2024

2024-03-29T21:40:20Z

Arl122 at 02:25, 23 February 2023

2023-02-23T02:25:05Z

Arl122 at 02:23, 23 February 2023

2023-02-23T02:23:15Z

Arl122: Created page with "In this project we explore several ideas including: Student self-explanations when working with solved programming exercises by gauging their similarity with expected expert..."

2023-02-23T02:23:04Z

Created page with "In this project we explore several ideas including: Student self-explanations when working with solved programming exercises by gauging their similarity with expected expert..."

New page

In this project we explore several ideas including:

Student self-explanations when working with solved programming exercises by gauging their similarity with expected expert self-explanations [1].

'''References'''

1. Rus, Vasile & Akhuseyinoglu, Kamil & Chapagain, Jeevan & Tamang, Lasang & Brusilovsky, Peter. (2022). Prompting for Free Self-Explanations Promotes Better Code Comprehension.
2.

← Older revision		Revision as of 22:05, 29 March 2024
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	<h1>Investigating and Scaffolding Students' Mental Models during Computer Programming Tasks to Improve Learning, Engagement, and Retention</h1>		<h1>Investigating and Scaffolding Students' Mental Models during Computer Programming Tasks to Improve Learning, Engagement, and Retention</h1>
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−	~~In this project, we explore several ideas including:~~
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−	~~Student self-explanations when working with solved programming exercises by gauging their similarity with expected expert self-explanations [1].~~

	== About the project ==		== About the project ==

@@ Line 19: / Line 19: @@
 ===University of Pittsburgh Team===
-* '''PIs:''' Vasile Rus, [[User:Peterb|Peter Brusilovsky]]
+* '''PI:''' [[User:Peterb|Peter Brusilovsky]]
 * Graduate researchers: Zak Risha, Kamil Akhuseyinoglu, Arun LR

@@ Line 8: / Line 8: @@
 == About the project ==
-The project was originally funded by the University of Pittsburgh through the [https://www.provost.pitt.edu/priorities/personalizing-education Personalized Education] program (2018-2020), however, it is still going on, several years after the end of the two original rounds of funding. In the course of the project, we developed and evaluated several versions of the interactive recommender system Grapevine.
+This project is a joint effort of research teams at the University of Memphis and the University of Pittsburgh to explore the process of code comprehension by students and build a AI-based tools to support practice and assessment of code comprehension. The project was funded by the National Science Foundation by [https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822752&HistoricalAwards=false Award 1822752] between 2018 and 2023. In the course of the project, we developed and evaluated open-learned modeling approaches, code-tracing tools to practice code comprehension, and an intelligent tutor for assessing code comprehension through explanations.
 == Motivation ==
 == Teams ==
@@ Line 32: / Line 34: @@
 * Alshaikh, Z., Tamang, L. J., and Rus, V. (2020) Experiments with a Socratic Intelligent Tutoring System for Source Code Understanding. In:  Proceedings of The Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-32), Miami, FL, Association for the Advancement of Artificial Intelligence, pp. 457-460.
 * Ait Khayi, N. and Rus, V. (2020) Attention Based Transformer for Student Answers Assessment. In:  Proceedings of The Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-32), Miami, FL, Association for the Advancement of Artificial Intelligence, pp. 3-8.
 * Rus, V., Akhuseyinoglu, K., Chapagain, J., Tamang, L., and Brusilovsky, P. (2021) Prompting for Free Self-Explanations Promotes Better Code Comprehension. . In:  Proceedings of 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop at EDM2021, Paris, France, Julne 29, 2021, CEUR.
 * Chapagain, J., Tamang, L., Banjade, R., Oli, P., and Rus, V. (2022) Automated Assessment of Student Self-explanation During Source Code Comprehension. In:  Proceedings of The Thirty-Fifth International Florida Artificial Intelligence Research Society Conference (FLAIRS-35), Jensen Beach, FL, May 15-18, 2022, Association for the Advancement of Artificial Intelligence.

@@ Line 33: / Line 33: @@
 * Ait Khayi, N. and Rus, V. (2020) Attention Based Transformer for Student Answers Assessment. In:  Proceedings of The Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-32), Miami, FL, Association for the Advancement of Artificial Intelligence, pp. 3-8.
 * Rus, V., Akhuseyinoglu, K., Chapagain, J., Tamang, L., and Brusilovsky, P. (2021) Prompting for Free Self-Explanations Promotes Better Code Comprehension. . In:  Proceedings of 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop at EDM2021, Paris, France, Julne 29, 2021, CEUR.
-Chapagain, J., Tamang, L., Banjade, R., Oli, P., and Rus, V. (2022) Automated Assessment of Student Self-explanation During Source Code Comprehension. In:  Proceedings of The Thirty-Fifth International Florida Artificial Intelligence Research Society Conference (FLAIRS-35), Jensen Beach, FL, May 15-18, 2022, Association for the Advancement of Artificial Intelligence.
+* Chapagain, J., Tamang, L., Banjade, R., Oli, P., and Rus, V. (2022) Automated Assessment of Student Self-explanation During Source Code Comprehension. In:  Proceedings of The Thirty-Fifth International Florida Artificial Intelligence Research Society Conference (FLAIRS-35), Jensen Beach, FL, May 15-18, 2022, Association for the Advancement of Artificial Intelligence.
 * Rus, V., Brusilovsky, P., Tamang, L. J., Akhuseyinoglu, K., and Fleming, S. (2022) DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning. In: S. Crossley and E. Popescu (eds.) Proceedings of 18th International Conference on Intelligent Tutoring Systems, ITS 2022, Bucharest, Romania, June 29 - July 1, 2022, Springer International Publishing, pp. 36--50.
-* Banjade, R., Oli, P., Tamang, L. J., and Rus, V. (2022) Preliminary Experiments with Transformer based Approaches To Automatically Inferring Domain Models from Textbooks
+* Banjade, R., Oli, P., Tamang, L. J., and Rus, V. (2022) Preliminary Experiments with Transformer based Approaches To Automatically Inferring Domain Models from Textbooks. In: A. Mitrovic and N. Bosch (eds.) Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022), Durham, UK, July 24-27, 2022, pp. 667--672.
 * Tamang, L. J., Banjade, R., Chapagain, J., and Rus, V. (2022) Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension. In: M. M. Rodrigo, N. Matsuda, A. I. Cristea and V. Dimitrova (eds.) Proceedings of 23rd International Conference on Artificial Intelligence in Education, AIED 2022, Part 1, Durham, UK, July 27–31, 2022, Springer, pp. 743-748.
 * Oli, P., Rus, V., Banjade, R., Narayanan, A. L., and Brusilovsky, P. (2023) When is reading more effective than tutoring? An analysis through the lens of students' self-efficacy among novices in computer science. . In:  Proceedings of 7th Educational Data Mining in Computer Science Education (CSEDM) Workshop at LAK 2023, Arlington, TX, March 13, 2023.

@@ Line 6: / Line 6: @@
 Student self-explanations when working with solved programming exercises by gauging their similarity with expected expert self-explanations [1].
-'''References'''
+== About the project ==
-. Rus, Vasile & Akhuseyinoglu, Kamil & Chapagain, Jeevan & Tamang, Lasang & Brusilovsky, Peter. (2022). Prompting for Free Self-Explanations Promotes Better Code Comprehension.
+The project was originally funded by the University of Pittsburgh through the [https://www.provost.pitt.edu/priorities/personalizing-education Personalized Education] program (2018-2020), however, it is still going on, several years after the end of the two original rounds of funding. In the course of the project, we developed and evaluated several versions of the interactive recommender system Grapevine.
-.
+== Motivation ==

@@ Line 1: / Line 1: @@
-<h1>CSEdPad: Investigating and Scaffolding Students' Mental Models during Computer Programming Tasks to Improve Learning, Engagement, and Retention</h1>
+<h1>Investigating and Scaffolding Students' Mental Models during Computer Programming Tasks to Improve Learning, Engagement, and Retention</h1>
-In this project we explore several ideas including:
+In this project, we explore several ideas including:
 Student self-explanations when working with solved programming exercises by gauging their similarity with expected expert self-explanations [1].

← Older revision		Revision as of 02:25, 23 February 2023
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		+	<h1>CSEdPad: Investigating and Scaffolding Students' Mental Models during Computer Programming Tasks to Improve Learning, Engagement, and Retention</h1>
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	In this project we explore several ideas including:		In this project we explore several ideas including:

← Older revision		Revision as of 02:23, 23 February 2023
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	1. Rus, Vasile & Akhuseyinoglu, Kamil & Chapagain, Jeevan & Tamang, Lasang & Brusilovsky, Peter. (2022). Prompting for Free Self-Explanations Promotes Better Code Comprehension.		1. Rus, Vasile & Akhuseyinoglu, Kamil & Chapagain, Jeevan & Tamang, Lasang & Brusilovsky, Peter. (2022). Prompting for Free Self-Explanations Promotes Better Code Comprehension.
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	2.		2.