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We conducted a controlled study to investigate whether having students choose the concept on which to solve each practice problem in an adaptive tutor helped improve learning. We analyzed data from an adaptive tutor used by introductory programming students over three semesters. The tutor presented code-tracing problems, used pretest-practice-post-test protocol, and presented line-by-line explanation of the correct solution as feedback. We found that choice did not in-crease the amount of learning or pace of learning. But, it resulted in greater improvement in score on learned concepts, and the effect size was medium.
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An Epistemic Model-Based Tutor for Imperative Programming
We developed a tutor for imperative programming in C++. It covers algorithm formulation, program design and coding – all three stages involved in writing a program to solve a problem. The design of the tutor is epistemic, i.e., true to real-life programming practice. The student works through all the three stages of programming in interleaved fashion, and within the context of a single code canvas. The student has the sole agency to compose the program and write the code. The tutor uses goals and plans as prompts to scaffold the student through the programming process designed by an expert. It provides drill-down immediate feed-back at the abstract, concrete and bottom-out levels at each step. So, by the end of the session, the student is guaranteed to write the complete and correct program for a given problem. We used model-based architecture to implement the tutor be-cause of the ease with which it facilitates adding problems to the tutor. In a preliminary study, we found that practicing with the tutor helped students solve problems with fewer erroneous actions and less time.
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- Award ID(s):
- 1432190
- PAR ID:
- 10301921
- Date Published:
- Journal Name:
- Artificial Intelligence in Educaiton
- Page Range / eLocation ID:
- 213-218
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-78270-2_38
