
NSF Org: |
DUE Division Of Undergraduate Education |
Recipient: |
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Initial Amendment Date: | September 2, 2010 |
Latest Amendment Date: | September 14, 2012 |
Award Number: | 1021975 |
Award Instrument: | Standard Grant |
Program Manager: |
Michael Erlinger
DUE Division Of Undergraduate Education EDU Directorate for STEM Education |
Start Date: | September 1, 2010 |
End Date: | August 31, 2015 (Estimated) |
Total Intended Award Amount: | $348,089.00 |
Total Awarded Amount to Date: | $348,089.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 (765)494-1055 |
Sponsor Congressional District: |
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Primary Place of Performance: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
S-STEM-Schlr Sci Tech Eng&Math, CCLI-Type 2 (Expansion) |
Primary Program Source: |
1300XXXXDB H-1B FUND, EDU, NSF |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.076 |
ABSTRACT
Alice is a high-impact, high-interest program visualization environment, that is increasingly being used to teach novices object-oriented programming in a range of courses. Results provide evidence that using Alice can significantly improve achievement, retention and recruitment of high-risk CS majors during their first year. This project is expanding the capabilities of Alice by integrating an intelligent tutoring system (ITS). The ITS has three major functions: 1) intelligent delivery of individualized instructional materials, 2) automatic detection of off-task student behavior and 3) the ability to alert instructors that a student may be having difficulties. Expected project outcomes include the enhanced version of Alice with the ITS and additional curricular materials. Assessment is designed to determine if student achievement and attitudes improve as a result of using the ITS. The PIs expect to grow the community of ITS developers for computing education. The Alice community includes hundreds of college faculty and high school teachers across a diverse collection of settings and has been used by thousands of students.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Effective Intelligent Tutoring Systems
Based on our experience with building the tutoring systems and conducting our research, we think that the following should be the important components for an intelligent tutoring system:
• The design of the tutoring system should be based on the type of supervision. In a school-like environment, where students’ performance can be judged by supervisors, it would be better to have a Tutoring system like the Type 1 as it can speedup learning. On the other hand, tutoring systems focusing on self-learning or online learning without external supervisors where the system plays an important role in judging the progress of a student, Type 2 tutoring systems would be preferred.
• A clearly designed workflow that allows students to track their progress is crucial.
• The tutorial should be short and at the same time should teach generic concepts as opposed to focusing on the student solving a particular task.
• Having pop-ups should be based on the audience targeted. For much younger learners (middle or primary school), having them can be beneficial.
• The time limit allowed to complete a task should be based on a student’s exposure to Alice. We recommend anywhere between 15-30 minutes for a typical Alice Program.
• Having multiple contexts for tasks of similar levels prevents the student from losing interest and at the same time helps in grasping the concept.
• Feedback at the end of each task is very important. If students are made to switch contexts, they should be aware of the reasons, for example, they took more time than average to solve a program.
• Tutorials are not useful after a point. Deciding the number of times a tutorial should be repeated after failing activities at the same level of difficulty should depend on the target audience. For high school students, making them repeat the tutorial more than twice might make them lose interest in the tutoring system and hinder learning.
• Having a simple rule-based judger to decide student progress can be effective to ensure that the student actually learns the concepts and avoids external supervision.
• In a tutoring system with rule-based judgers, careful attention must be paid to the selection of tutorials and activities as some programs can be solved in multiple ways and hence, the judging system might fail for a different but correct solution.
Conclusion
In this research, we created three tutorials that we constructed on Alice to closely resemble the design of Intelligent Tutoring systems. We presented the results of the case studies on these tutoring system. We have discussed what we think are the essential components for an intelligent tutorial system. As we conducted the case studies on a fairly small number of students, we suggest that these studies be repeated with larger groups to further reinforce the results of the studies.
Last Modified: 11/30/2015
Modified by: Hubert E Dunsmore
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