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12th International Conference on Intelligent Tutoring Systems (ITS)

作者： Lazar, Timotej Bratko, Ivan Univ Ljubljana Fac Comp & Informat Sci Ljubljana 61000 Slovenia

ISBN: (纸本)9783319072210;9783319072203

One of the main functions of intelligent tutoring systems is providing feedback to help students solve problems. We present a novel approach to program synthesis that can be used as a basis for automatic hint generation in programming tutors. Instead of using a state-space representation of the problem-solving process, our method finds a set of textual edits commonly used by students on program code. Given an incorrect program it then synthesizes new programs by applying sequences of edits until a solution is found. The edit sequence can be used to provide hints with varying levels of detail. Experimental results confirm the feasibility of our approach.

关键词： programming tutors hint generation program synthesis

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Identifying typical approaches and errors in Prolog programming with argument-based machine learning

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 112卷 110-124页

作者： Mozina, Martin Lazar, Timotej Bratko, Ivan Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia

Students learn programming much faster when they receive feedback. However, in programming courses with high student-teacher ratios, it is practically impossible to provide feedback to all homeworks submitted by students. In this paper, we propose a data-driven tool for semi-automatic identification of typical approaches and errors in student solutions. Having a list of frequent errors, a teacher can prepare common feedback to all students that explains the difficult concepts. We present the problem as supervised rule learning, where each rule corresponds to a specific approach or error. We use correct and incorrect submitted programs as the learning examples, where patterns in abstract syntax trees are used as attributes. As the space of all possible patterns is immense, we needed the help of experts to select relevant patterns. To elicit knowledge from the experts, we used the argument-based machine learning (ABML) method, in which an expert and ABML interactively exchange arguments until the model is good enough. We provide a step-by-step demonstration of the ABML process, present examples of ABML questions and corresponding expert's answers, and interpret some of the induced rules. The evaluation on 42 Prolog exercises further shows the usefulness of the knowledge elicitation process, as the models constructed using ABML achieve significantly better accuracy than the models learned from human-defined patterns or from automatically extracted patterns. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Argument-based machine learning Rule learning programming tutors Abstract syntax tree Syntactic patterns

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Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python programming Tutor

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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2017年第1期27卷 37-64页

作者： Rivers, Kelly Koedinger, Kenneth R. Carnegie Mellon Univ Pittsburgh PA 15213 USA

To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not occurred in the data before. We provide a detailed description of the system's implementation and perform a technical evaluation on a small set of data to determine the effectiveness of the component algorithms and ITAP's potential for self-improvement. The results show that ITAP is capable of producing hints for almost any given state after being given only a single reference solution, and that it can improve its performance by collecting data over time.

关键词： Data-driven tutoring Automatic hint generation programming tutors Solution space

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Retention in First Stage Undergraduate Computing: Lessons Learned from a Collaborative Learning Intervention 2023

Retention in First Stage Undergraduate Computing: Lessons Le...

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28th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE)

作者： Scott, Michael James Mitchell, Alexander Brown, Douglas Falmouth Univ Games Acad Cornwall England

ISBN: (纸本)9798400701399

It is challenging to retain computing students through their first stage of undergraduate education. Attrition is high, with many transferring courses or dropping out. This poster explores preliminary findings from an action research project improving continuation in first-stage undergraduate computing. Five years of data from Falmouth University's Games Academy in the UK suggest improvement in first-stage retention from 66.6% in 2017-18 to 91.2% in 2021-22. Findings support prior work on pair programming, media computation, and peer instruction. However, they also highlight the benefits of collaborative learning facilitated by faculty and informed by learning analytics. Peer reviews and pre-submission clinics, student advisor follow-ups, and retrieval via synoptic assessment also contributed to the improvement.

关键词： action research retention continuation completion progression learning analytics collaborative learning formative feedback student advisors synoptic assessment programming tutors

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Comparing Bayesian Knowledge Tracing Model Against Naive Mastery Model 17th

Comparing Bayesian Knowledge Tracing Model Against Naive Mas...

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17th International Conference on Intelligent Tutoring Systems (ITS)

作者： Getseva, Vanesa Kumar, Amruth N. Ramapo Coll Mahwah NJ 07430 USA

ISBN: (纸本)9783030804213;9783030804206

We conducted a study to see if using Bayesian Knowledge Tracing (BKT) models would save time and problems in programming tutors. We used legacy data collected by two programming tutors to compute BKT models for every concept covered by each tutor. The novelty of our model was that slip and guess parameters were computed for every problem presented by each tutor. Next, we used cross-validation to evaluate whether the resulting BKT model would have reduced the number of practice problems solved and time spent by the students represented in the legacy data. We found that in 64.23% of the concepts, students would have saved time with the BKT model. The savings varied among concepts. Overall, students would have saved a mean of 1.28 min and 1.23 problems per concept. We also found that BKT models were more effective at saving time and problems on harder concepts.

关键词： programming tutors Bayesian Knowledge Tracing Evaluation

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Designing an intelligent tutoring system for computer programing in the Pacific

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EDUCATION AND INFORMATION TECHNOLOGIES 2022年第5期27卷 6197-6209页

作者： Sharma, Priynka Harkishan, Mayuri Univ South Pacific Suva Sch Informat Technol Engn Math & Phys STEMP Laucala Campus Suva Fiji

Intelligent Tutoring Systems (ITSs) are educational systems that reflect knowledge using artificial intelligence implements. In this paper, we give an outline of the programming-Tutor architectural design with the core implements on user interaction. This pilot proposal is for designing a model domain of a subset in the computer programming language. The completed project would be adequate to show the idea of a completely developed computing Intelligent Tutoring System in online programming courses to offer benefits to students in the Pacific. This proposed concept would also provide students with an immersive learning experience in an online course to assist in a formative assessment to enhance student learning. A smart tutoring system can provide prompt input of high quality which not only conveys to students about the consistency of the solution but also provides them with information on the precision of the key concerning their existing solutions expertise. This Intelligent Tutoring System (ITS) is proposed to be designed using intelligent algorithms such as optimized ant colony to be able to support the online tutoring system that can initiate the complex learning principles in computing science courses. It is also hypothesized that, based on the performance of other Intelligent Tutoring Systems, students would be able to learn to program more easily in regional campuses and acquire experiences more rapidly and efficiently than students who are taught using conventional methods in an online mode.

关键词： Intelligent tutoring system Education programming tutors Software design E-learning Artificial intelligence

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Evaluation of Guided-Planning and Assisted-Coding with Task Relevant Dynamic Hinting

Evaluation of Guided-Planning and Assisted-Coding with Task ...

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12th International Conference on Intelligent Tutoring Systems (ITS)

作者： Jin, Wei Corbett, Albert Lloyd, Will Baumstark, Lewis Rolka, Christine Univ West Georgia Carrollton GA 30118 USA Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9783319072210;9783319072203

We describe a programming tutor framework that consists of two configurable components, a guided-planning component and an assisted-coding component that offers task relevant automatically-generated hints on demand to students. We evaluate the effectiveness of the new integrated planning and coding environment by comparing it to three other tutor conditions: planning-only, coding-only, and planning-only interleaved with planning-coding. We conclude that the integrated planning and coding tutor environment is more effective than tutored planning-only activities and that students make more efficient use of tutor feedback in the integrated environment than in the coding only environment.

关键词： Intelligent tutoring systems automatic hint generation programming tutors

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Automatic Extraction of AST Patterns for Debugging Student Programs

Automatic Extraction of AST Patterns for Debugging Student P...

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18th International Conference on Artificial Intelligence in Education (AIED)

作者： Lazar, Timotej Mozina, Martin Bratko, Ivan Univ Ljubljana Fac Comp & Informat Sci Ljubljana Slovenia

ISBN: (纸本)9783319614250;9783319614243

When implementing a programming tutor it is often difficult to manually consider all possible errors encountered by students. An alternative is to automatically learn a bug library of erroneous patterns from students' programs. We propose abstract-syntax-tree (AST) patterns as features for learning rules to distinguish between correct and incorrect programs. We use these rules to debug student programs: rules for incorrect programs (buggy rules) indicate mistakes, whereas rules for correct programs group programs with the same solution strategy. To generate hints, we first check buggy rules and point out incorrect patterns. If no buggy rule matches, we use rules for correct programs to recognize the student's intent and suggest missing patterns. We evaluate our approach on past student programming data for a number of Prolog problems. For 31 out of 44 problems, the induced rules correctly classify over 85% of programs based only on their structural features. For approximately 73% of incorrect submissions, we are able to generate hints that were implemented by the student in some subsequent submission.

关键词： programming tutors Error diagnosis Hint generation Abstract syntax tree Syntactic features

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An Empirical Analysis of Code-Tracing Concepts 22

An Empirical Analysis of Code-Tracing Concepts

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27th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE)

作者： Getseva, Vanesa Kumar, Amruth N. Ramapo Coll Comp Sci Mahwah NJ 07430 USA

ISBN: (纸本)9781450392013

Which code-tracing concepts are introductory programming students likely to learn from classroom instruction and which ones need additional problem-solving practice to master? Are there relationships among programming concepts that can be used to build adaptive assessment instruments? To answer these questions, we analyzed the data collected over several semesters by a suite of code-tracing tutors called problets, that administered pre-test, practice, post-test protocol. Each tutor covered a single programming topic, which consisted of 9-25 concepts. For each concept, we used the pretest data to calculate the probability that students knew the concept before using the tutor. Using a weighted average of the concept probabilities, we found that students had learned some topics more than others: if/if-else (0.85), function behavior (0.76), arrays (0.73), while (0.7), for (0.69), switch (0.67), and debugging functions (0.55). Some of the concepts on which students needed additional practice included bugs, nested loops and back-to-back loops. Expressions, even when used in novel contexts, were not challenging for students. We built a Bayesian network for each topic based on conditional probabilities to discover the concepts that must be covered, and those whose coverage is redundant in the presence of other concepts. A strength of this empirical study is that it uses a large dataset collected from multiple institutions over multiple semesters. We also list threats to the validity of the study.

关键词： Code-tracing programming tutors Difficulty of concepts

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Automated Data-Driven Hint Generation for Learning programming

Automated Data-Driven Hint Generation for Learning Programmi...

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作者： Rivers, Kelly Carnegie Mellon University

学位级别：Ph.D.

Feedback is an essential component of the learning process, but in fields like computer science, which have rapidly increasing class sizes, it can be difficult to provide feedback to students at scale. Intelligent tutoring systems can provide personalized feedback to students automatically, but they can take large amounts of time and expert knowledge to build, especially when determining how to give students hints. Data-driven approaches can be used to provide personalized next-step hints automatically and at scale, by mining previous students’ solutions. I have created ITAP, the Intelligent Teaching Assistant for programming, which automatically generates next-step hints for students in basic Python programming assignments. ITAP is composed of three stages: canonicalization, where a student's code is transformed to an abstracted representation; path construction, where the closest correct state is identified and a series of edits to that goal state are generated; and reification, where the edits are transformed back into the student's original context. With these techniques, ITAP can generate next-step hints for 100% of student submissions, and can even chain these hints together to generate a worked example. Initial analysis showed that hints could be used in practice problems in a real classroom environment, but also demonstrated that students' relationships with hints and help-seeking were complex and required deeper investigation. In my thesis work, I surveyed and interviewed students about their experience with help-seeking and using feedback, and found that students wanted more detail in hints than was initially provided. To determine how hints should be structured, I ran a usability study with programmers at varying levels of knowledge, where I found that more novice students needed much higher levels of content and detail in hints than was traditionally given. I also found that examples were commonly used in the learning process, and could serve an integral

关键词： data-driven tutoring programming tutors canonicalization path construction hint representation hint evaluation self-improving tutoring system

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