Automated Data-Driven Hint Generation in Intelligent Tutoring Systems for Code-Writing: On the Road of Future Research.
Introductory programming is an essential part of the curriculum in any engineering discipline in universities. However, for many beginning students, it is very difficult to learn. In particular, these students often get stuck and frustrated when attempting to solve programming exercises. One way...
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| Main Authors: | , |
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| Format: | Journal |
| Language: | en |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://ur.aeu.edu.my/501/1/Automated%20Data-Driven%20Hint%20Generation%20in.pdf http://ur.aeu.edu.my/501/ |
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| Summary: | Introductory programming is an essential part of the curriculum
in any engineering discipline in universities. However, for many beginning students,
it is very difficult to learn. In particular, these students often get stuck
and frustrated when attempting to solve programming exercises. One way to assist
beginning programmers to overcome difficulties in learning to program is to
use intelligent tutoring systems (ITSs) for programming, which can provide
students with personalized hints of students’ solving process in programming
exercises.
Currently, mostly these systems manually construct the domain models.
They take much time to construct, especially for exercises with very large solution
spaces. One of the major challenges associated with handling ITSs for programming
comes from the diversity of possible code solutions that a student
can write. The use of data-driven approaches to develop these ITSs is just starting
to be explored in the field. Given that this is still a relatively new research
field, many challenges are still remained unsolved. Our goal in this paper is to
review and classify analysis techniques that are requested to generate datadriven
hints in ITSs for programming. This work also aims equally to identify
the possible future directions in this research field. |
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