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243 results on '"Desmarais, Michel C."'

1. Bugs in Large Language Models Generated Code: An Empirical Study

Author

Tambon, Florian, Dakhel, Arghavan Moradi, Nikanjam, Amin, Khomh, Foutse, Desmarais, Michel C., and Antoniol, Giuliano

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract

Large Language Models (LLMs) for code have gained significant attention recently. They can generate code in different programming languages based on provided prompts, fulfilling a long-lasting dream in Software Engineering (SE), i.e., automatic code generation. Similar to human-written code, LLM-generated code is prone to bugs, and these bugs have not yet been thoroughly examined by the community. Given the increasing adoption of LLM-based code generation tools (e.g., GitHub Copilot) in SE activities, it is critical to understand the characteristics of bugs contained in code generated by LLMs. This paper examines a sample of 333 bugs collected from code generated using three leading LLMs (i.e., CodeGen, PanGu-Coder, and Codex) and identifies the following 10 distinctive bug patterns: Misinterpretations, Syntax Error, Silly Mistake, Prompt-biased code, Missing Corner Case, Wrong Input Type, Hallucinated Object, Wrong Attribute, Incomplete Generation, and Non-Prompted Consideration. The bug patterns are presented in the form of a taxonomy. The identified bug patterns are validated using an online survey with 34 LLM practitioners and researchers. The surveyed participants generally asserted the significance and prevalence of the bug patterns. Researchers and practitioners can leverage these findings to develop effective quality assurance techniques for LLM-generated code. This study sheds light on the distinctive characteristics of LLM-generated code., Comment: 47 pages, 7 figures

Published
2024

2. Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing

Author

Dakhel, Arghavan Moradi, Nikanjam, Amin, Majdinasab, Vahid, Khomh, Foutse, and Desmarais, Michel C.

Subjects
Computer Science - Software Engineering
Abstract

One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test generation tools is to ease the development of tests by suggesting efficient bug-revealing tests. Recently, researchers have leveraged Large Language Models (LLMs) of code to generate unit tests. While the code coverage of generated tests was usually assessed, the literature has acknowledged that the coverage is weakly correlated with the efficiency of tests in bug detection. To improve over this limitation, in this paper, we introduce MuTAP for improving the effectiveness of test cases generated by LLMs in terms of revealing bugs by leveraging mutation testing. Our goal is achieved by augmenting prompts with surviving mutants, as those mutants highlight the limitations of test cases in detecting bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs). We employ different LLMs within MuTAP and evaluate their performance on different benchmarks. Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets. Among these, 17% remained undetected by both the current state-of-the-art fully automated test generation tool (i.e., Pynguin) and zero-shot/few-shot learning approaches on LLMs. Furthermore, MuTAP achieves a Mutation Score (MS) of 93.57% on synthetic buggy code, outperforming all other approaches in our evaluation. Our findings suggest that although LLMs can serve as a useful tool to generate test cases, they require specific post-processing steps to enhance the effectiveness of the generated test cases which may suffer from syntactic or functional errors and may be ineffective in detecting certain types of bugs and testing corner cases PUTs., Comment: 16 pages, 3 figures

Published
2023

3. Alloprof: a new French question-answer education dataset and its use in an information retrieval case study

Author

Lefebvre-Brossard, Antoine, Gazaille, Stephane, and Desmarais, Michel C.

Subjects
Computer Science - Computation and Language, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Teachers and students are increasingly relying on online learning resources to supplement the ones provided in school. This increase in the breadth and depth of available resources is a great thing for students, but only provided they are able to find answers to their queries. Question-answering and information retrieval systems have benefited from public datasets to train and evaluate their algorithms, but most of these datasets have been in English text written by and for adults. We introduce a new public French question-answering dataset collected from Alloprof, a Quebec-based primary and high-school help website, containing 29 349 questions and their explanations in a variety of school subjects from 10 368 students, with more than half of the explanations containing links to other questions or some of the 2 596 reference pages on the website. We also present a case study of this dataset in an information retrieval task. This dataset was collected on the Alloprof public forum, with all questions verified for their appropriateness and the explanations verified both for their appropriateness and their relevance to the question. To predict relevant documents, architectures using pre-trained BERT models were fine-tuned and evaluated. This dataset will allow researchers to develop question-answering, information retrieval and other algorithms specifically for the French speaking education context. Furthermore, the range of language proficiency, images, mathematical symbols and spelling mistakes will necessitate algorithms based on a multimodal comprehension. The case study we present as a baseline shows an approach that relies on recent techniques provides an acceptable performance level, but more work is necessary before it can reliably be used and trusted in a production setting.

Published
2023

5. An Overview on Large Language Models

Author

Dakhel, Arghavan Moradi, Nikanjam, Amin, Khomh, Foutse, Desmarais, Michel C., Washizaki, Hironori, Nguyen-Duc, Anh, editor, Abrahamsson, Pekka, editor, and Khomh, Foutse, editor

Published
2024
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6. Dev2vec: Representing Domain Expertise of Developers in an Embedding Space

Author

Dakhel, Arghavan Moradi, Desmarais, Michel C., and Khomh, Foutse

Subjects
Computer Science - Software Engineering, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Accurate assessment of the domain expertise of developers is important for assigning the proper candidate to contribute to a project or to attend a job role. Since the potential candidate can come from a large pool, the automated assessment of this domain expertise is a desirable goal. While previous methods have had some success within a single software project, the assessment of a developer's domain expertise from contributions across multiple projects is more challenging. In this paper, we employ doc2vec to represent the domain expertise of developers as embedding vectors. These vectors are derived from different sources that contain evidence of developers' expertise, such as the description of repositories that they contributed, their issue resolving history, and API calls in their commits. We name it dev2vec and demonstrate its effectiveness in representing the technical specialization of developers. Our results indicate that encoding the expertise of developers in an embedding vector outperforms state-of-the-art methods and improves the F1-score up to 21%. Moreover, our findings suggest that ``issue resolving history'' of developers is the most informative source of information to represent the domain expertise of developers in embedding spaces., Comment: 30 pages, 5 figures

Published
2022
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7. GitHub Copilot AI pair programmer: Asset or Liability?

Author

Dakhel, Arghavan Moradi, Majdinasab, Vahid, Nikanjam, Amin, Khomh, Foutse, Desmarais, Michel C., Ming, Zhen, and Jiang

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract

Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired., Comment: 27 pages, 8 figures

Published
2022

8. Methodology of Measure of Similarity in Student Video Sequence of Interactions

Author

Mbouzao, Boniface, Desmarais, Michel C., and Shrier, Ian

Abstract

Massive online Open Courses (MOOCs) make extensive use of videos. Students interact with them by pausing, seeking forward or backward, replaying segments, etc. We can reasonably assume that students have different patterns of video interactions, but it remains hard to compare student video interactions. Some methods were developed, such as Markov Chain and Edit Distance. However, these methods have caveats as we show with prototypical examples. This paper proposes a new methodology of comparing video sequences of interaction based both on time spent in each state and the succession of states by computing the distance between the transition matrices of the video interaction sequences. Results show the proposed methodology can better characterize video interaction in a task to discriminate which student is interacting with a video, or which video a student is interacting with. [For the full proceedings, see ED607784.]

Published
2020

9. A Dataset of Learnersourced Explanations from an Online Peer Instruction Environment

Author

Bhatanagar, Sameer, Zouaq, Amal, Desmarais, Michel C., and Charles, Elizabeth

Abstract

Online "Peer Instruction" has become prevalent in many "flipped classroom" settings, yet little work has been done to examine the content students generate in such a learning environment. This study characterizes a dataset generated by an open-source, web-based homework system that prompts students to first answer questions, and then provide explanations of their reasoning. Of particular interest in this dataset, is that students are also prompted to evaluate a subset of peer explanations based on how convincing they are, as part of the Peer Instruction learning script. Since these student "votes" are then used in the selection of what is shown to future learners, we cast this as an instance of "learnersourcing," a paradigm that presents new research opportunities for the Learning Analytics community. This study characterizes a dataset from one "Peer Instruction" tool, that includes not only the student generated answers and explanations, but this novel "vote" attribute, which aims to capture how convincing each explanation is to other learners. The dataset includes longitudinal observations of student responses over the course of a semester, following groups from three STEM disciplines. The data is made available to interested researchers. [For the full proceedings, see ED607784.]

Published
2020

12. An Empirical Research on Identifiability and Q-Matrix Design for DINA Model

Author

Xu, Peng and Desmarais, Michel C.

Abstract

In most contexts of student skills assessment, whether the test material is administered by the teacher or within a learning environment, there is a strong incentive to minimize the number of questions or exercises administered in order to get an accurate assessment. This minimization objective can be framed as a Q-matrix design problem: given a set of skills to assess and a fixed number of question items, determine the optimal set of items, out of a potentially large pool, that will yield the most accurate assessment. In recent years, the Q-matrix identifiability under DINA/DINO models has been proposed as a guiding principle for that purpose. We empirically investigate the extent to which identifiability can serve that purpose. Identifiability of Q-matrices is studied throughout a range of conditions in an effort to measure and understand its relation to student skills assessment. The investigation relies on simulation studies of skills assessment with synthetic data. Results show that identifiability is an important factor that determines the capacity of a Q-matrix to lead to accurate skills assessment with the least number of questions. [For the full proceedings, see ED593090.]

Published
2018

14. Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing

Author

Minn, Sein, Yu, Yi, Desmarais, Michel C., Zhu, Feida, and Vie, Jill Jenn

Subjects
Computer Science - Artificial Intelligence
Abstract

In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling., Comment: IEEE International Conference on Data Mining, 2018

Published
2018

16. Boosted Decision Tree for Q-Matrix Refinement

Author

Xu, Peng and Desmarais, Michel C.

Abstract

In recent years, substantial improvements were obtained in the effectiveness of data driven algorithms to validate the mapping of items to skills, or the Q-matrix. In the current study we use ensemble algorithms on top of existing Q-matrix refinement algorithms to improve their performance. We combine the boosting technique with a decision tree. The results show that the improvements from both the decision tree and Adaboost combined are better than the decision tree alone and yield substantial gains over the best performance of individual Q-matrix refinement algorithm. [For the full proceedings, see ED592609.]

Published
2016

17. Learnersourcing Quality Assessment of Explanations for Peer Instruction

Author

Bhatnagar, Sameer, Zouaq, Amal, Desmarais, Michel C., Charles, Elizabeth, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Alario-Hoyos, Carlos, editor, Rodríguez-Triana, María Jesús, editor, Scheffel, Maren, editor, Arnedillo-Sánchez, Inmaculada, editor, and Dennerlein, Sebastian Maximilian, editor

Published
2020
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18. Early Prediction of Success in MOOC from Video Interaction Features

Author

Mbouzao, Boniface, Desmarais, Michel C., Shrier, Ian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bittencourt, Ig Ibert, editor, Cukurova, Mutlu, editor, Muldner, Kasia, editor, Luckin, Rose, editor, and Millán, Eva, editor

Published
2020
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19. Goodness of Fit of Skills Assessment Approaches: Insights from Patterns of Real vs. Synthetic Data Sets

Author

International Educational Data Mining Society, Beheshti, Behzad, and Desmarais, Michel C.

Abstract

This study investigates the issue of the goodness of fit of different skills assessment models using both synthetic and real data. Synthetic data is generated from the different skills assessment models. The results show wide differences of performances between the skills assessment models over synthetic data sets. The set of relative performances for the different models create a kind of "signature" for each specific data. We conjecture that if this signature is unique, it is a good indicator that the corresponding model is a good fit to the data. [For complete proceedings, see ED560503.]

Published
2015

20. Combining Techniques to Refine Item to Skills Q-Matrices with a Partition Tree

Author

International Educational Data Mining Society, Desmarais, Michel C., Xu, Peng, and Beheshti, Behzad

Abstract

The problem of mapping items to skills is gaining interest with the emergence of recent techniques that can use data for both defining this mapping, and for refining mappings given by experts. We investigate the problem of refining mapping from an expert by combining the output of different techniques. The combination is based on a partition tree that combines the suggested refinements of three known techniques from the literature. Each technique is given as input a Q-matrix, that maps items to skills, and student test outcome data, and outputs a modified Q-matrix that constitutes suggested improvements. We test the accuracy of the partition tree combination techniques over both synthetic and real data. The results over synthetic data show a high improvement over the best single technique with a 86% error reduction on average for four different Q-matrices. For real data, the error reduction is 55%. In addition to the substantial error reduction, the partition tree refinements provide a much more stable performance than any single technique. These results suggest that the partition tree is a valuable refinement combination approach that can effectively take advantage of the complementarity of the Q-matrix refinement techniques. It brings the goal of using a data driven approach to refine the item to skill mapping closer to real applications, although challenges remain and are discussed. [For complete proceedings, see ED560503.]

Published
2015

23. Dynamic Student Classiffication on Memory Networks for Knowledge Tracing

Author

Minn, Sein, Desmarais, Michel C., Zhu, Feida, Xiao, Jing, Wang, Jianzong, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Yang, Qiang, editor, Zhou, Zhi-Hua, editor, Gong, Zhiguo, editor, Zhang, Min-Ling, editor, and Huang, Sheng-Jun, editor

Published
2019
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24. Methods to Find the Number of Latent Skills

Author

International Educational Data Mining Society, Beheshti, Behzad, Desmarais, Michel C., and Naceur, Rhouma

Abstract

Identifying the skills that determine the success or failure to exercises and question items is a difficult task. Multiple skills may be involved at various degree of importance, and skills may overlap and correlate. In an effort towards the goal of finding the skills behind a set of items, we investigate two techniques to determine the number of dominant latent skills. The Singular Value Decomposition (SVD) is a known technique to find latent factors. The singular values represent direct evidence of the strength of latent factors. Application of SVD to finding the number of latent skills is explored. We introduce a second technique based on a "wrapper" approach. Linear models with different number of skills are built, and the one that yields the best prediction accuracy through cross validation is considered the most appropriate. The results show that both techniques are effective in identifying the latent factors over synthetic data. An investigation with real data from the fraction algebra domain is also reported. Both the SVD and "wrapper" methods yield results that have no simple interpretation. (Contains 6 figures and 3 footnotes.) [This project was supported by funding from the MATI institute and by Canada's NSERC discovery program. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]

Published
2012

25. An ensemble approach to determine the number of latent dimensions and assess its reliability.

Author

Neishabouri, Asana and Desmarais, Michel C.

Abstract

AbstractDetermining the number of latent dimensions (LD) of a data set is a ubiquitous problem, for which numerous methods have been developed. We compare some of the most effective ones on synthetic data, which allows proper evaluation given that the true number of LD is known. Results show that their performance is sensitive to data set attributes such as sparsity, number of observations in relation to number of features, and underlying feature distributions. Results also show this sensitivity is different across methods. This observation brings us to devise an ensemble technique to combine LD estimates from multiple methods and achieve an estimate that is more reliable than any single method. We also demonstrate that the variance of the estimates across the single methods is a good indicator of the expected loss of the ensemble-based LD estimate. This observation leads, in turn, to deriving a method for the assessment of the reliability of the estimate. Finally, we discuss the practical implications of the findings. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Refinement of a Q-matrix with an Ensemble Technique Based on Multi-label Classification Algorithms

Author

Minn, Sein, Desmarais, Michel C., Fu, ShunKai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Verbert, Katrien, editor, Sharples, Mike, editor, and Klobučar, Tomaž, editor

Published
2016
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29. Toward a Refined Paradigm for Architecting Usable Systems

Author

Rafla, Tamer, Desmarais, Michel C., Robillard, Pierre N., Karat, John, editor, Vanderdonckt, Jean, editor, Abowd, Gregory, editor, Calvary, Gaëlle, editor, Carroll, John M., editor, Cockton, Gilbert, editor, Czerwinski, Mary, editor, Feiner, Steven, editor, Furtado, Elizabeth, editor, Höök, Kristina, editor, Jacob, Robert J.K., editor, Jeffries, Robin, editor, Johnson, Peter, editor, Nakakoji, Kumiyo, editor, Palanque, Philippe, editor, Pastor, Oscar, editor, PaternÒ, Fabio, editor, Pribeanu, Costin, editor, Salzman, Marilyn, editor, Schmandt, Chris, editor, Stolze, Markus, editor, Szwillus, Gerd, editor, Tscheligi, Manfred, editor, Veer, Gerrit vander, editor, Zhai, Shumin, editor, Seffah, Ahmed, editor, and Desmarais, Michel C., editor

Published
2009
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30. Human-Centered Software Engineering: Software Engineering Architectures, Patterns, and Sodels for Human Computer Interaction

Author

Seffah, Ahmed, Vanderdonckt, Jean, Desmarais, Michel C., Karat, John, editor, Vanderdonckt, Jean, editor, Abowd, Gregory, editor, Calvary, Gaëlle, editor, Carroll, John M., editor, Cockton, Gilbert, editor, Czerwinski, Mary, editor, Feiner, Steven, editor, Furtado, Elizabeth, editor, Höök, Kristina, editor, Jacob, Robert J.K., editor, Jeffries, Robin, editor, Johnson, Peter, editor, Nakakoji, Kumiyo, editor, Palanque, Philippe, editor, Pastor, Oscar, editor, PaternÒ, Fabio, editor, Pribeanu, Costin, editor, Salzman, Marilyn, editor, Schmandt, Chris, editor, Stolze, Markus, editor, Szwillus, Gerd, editor, Tscheligi, Manfred, editor, Veer, Gerrit vander, editor, Zhai, Shumin, editor, Seffah, Ahmed, editor, and Desmarais, Michel C., editor

Published
2009
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31. Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier

Author

Fu, Shunkai, Minn, Sein, Desmarais, Michel C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, and Perner, Petra, editor

Published
2014
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32. Heuristic Inspection to Assess Persuasiveness: A Case Study of a Mathematics E-learning Program

Author

Brangier, Eric, Desmarais, Michel C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, and Marcus, Aaron, editor

Published
2014
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33. An Introduction to Human-Centered Software Engineering : Integrating Usability in the Development Process

Author

Seffah, Ahmed, Gulliksen, Jan, Desmarais, Michel C., Karat, John, editor, Vanderdonckt, Jean, editor, Abowd, Gregory, editor, Calvary, Gaëlle, editor, Carroll, John, editor, Cockton, Gilbert, editor, Czerwinski, Mary, editor, Feiner, Steve, editor, Furtado, Elizabeth, editor, Höök, Kristiana, editor, Jacob, Robert, editor, Jeffries, Robin, editor, Johnson, Peter, editor, Nakakoji, Kumiyo, editor, Palanque, Philippe, editor, Pastor, Oscar, editor, Paternò, Fabio, editor, Pribeanu, Costin, editor, Salzman, Marilyn, editor, Schmandt, Chris, editor, Stolze, Markus, editor, Szwillus, Gerd, editor, Tscheligi, Manfred, editor, van der Veer, Gerrit, editor, Zhai, Shumin, editor, Seffah, Ahmed, editor, Gulliksen, Jan, editor, and Desmarais, Michel C., editor

Published
2005
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34. HCI, Usability and Software Engineering Integration: Present and Future

Author

Seffah, Ahmed, Desmarais, Michel C., Metzker, Eduard, Karat, John, editor, Vanderdonckt, Jean, editor, Abowd, Gregory, editor, Calvary, Gaëlle, editor, Carroll, John, editor, Cockton, Gilbert, editor, Czerwinski, Mary, editor, Feiner, Steve, editor, Furtado, Elizabeth, editor, Höök, Kristiana, editor, Jacob, Robert, editor, Jeffries, Robin, editor, Johnson, Peter, editor, Nakakoji, Kumiyo, editor, Palanque, Philippe, editor, Pastor, Oscar, editor, Paternò, Fabio, editor, Pribeanu, Costin, editor, Salzman, Marilyn, editor, Schmandt, Chris, editor, Stolze, Markus, editor, Szwillus, Gerd, editor, Tscheligi, Manfred, editor, van der Veer, Gerrit, editor, Zhai, Shumin, editor, Seffah, Ahmed, editor, Gulliksen, Jan, editor, and Desmarais, Michel C., editor

Published
2005
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35. A Matrix Factorization Method for Mapping Items to Skills and for Enhancing Expert-Based Q-Matrices

Author

Desmarais, Michel C., Naceur, Rhouma, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lane, H. Chad, editor, Yacef, Kalina, editor, Mostow, Jack, editor, and Pavlik, Philip, editor

Published
2013
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36. Combining Collaborative Filtering and Text Similarity for Expert Profile Recommendations in Social Websites

Author

Spaeth, Alexandre, Desmarais, Michel C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carberry, Sandra, editor, Weibelzahl, Stephan, editor, Micarelli, Alessandro, editor, and Semeraro, Giovanni, editor

Published
2013
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37. Item to Skills Mapping: Deriving a Conjunctive Q-matrix from Data

Author

Desmarais, Michel C., Beheshti, Behzad, Naceur, Rhouma, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cerri, Stefano A., editor, Clancey, William J., editor, Papadourakis, Giorgos, editor, and Panourgia, Kitty, editor

Published
2012
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38. Performance Comparison of Item-to-Item Skills Models with the IRT Single Latent Trait Model

Author

Desmarais, Michel C., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Konstan, Joseph A., editor, Conejo, Ricardo, editor, Marzo, José L., editor, and Oliver, Nuria, editor

Published
2011
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39. Cross-Channel Query Recommendation on Commercial Mobile Search Engine: Why, How and Empirical Evaluation

Author

Fu, Shunkai, Pi, Bingfeng, Zhou, Ying, Desmarais, Michel C., Wang, Weilei, Han, Song, Rao, Xunrong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Theeramunkong, Thanaruk, editor, Kijsirikul, Boonserm, editor, Cercone, Nick, editor, and Ho, Tu-Bao, editor

Published
2009
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40. Simple but Effective Porn Query Recognition by k-NN with Semantic Similarity Measure

Author

Fu, Shunkai, Desmarais, Michel C., Pi, Bingfeng, Zhou, Ying, Wang, Weilei, Zou, Gang, Han, Song, Rao, Xunrong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Li, Qing, editor, Feng, Ling, editor, Pei, Jian, editor, Wang, Sean X., editor, Zhou, Xiaofang, editor, and Zhu, Qiao-Ming, editor

Published
2009
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43. Just-in-Time Knowledge for Effective Hybrid Learning

Author

Desmarais, Michel C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fong, Joseph, editor, Kwan, Reggie, editor, and Wang, Fu Lee, editor

Published
2008
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44. Modelling Argument Quality in Technology-Mediated Peer Instruction.

Author

Bhatnagar, Sameer, Zouaq, Amal, and Desmarais, Michel C.

Subjects
PEER teaching, MACHINE learning, NATURAL language processing, SUPERVISED learning, ARGUMENT
Abstract

Learnersourcing is the process by which students submit content that enriches the bank of learning materials available to their peers, all as an authentic part of their learning experience. One example of learnersourcing is Technology-Mediated Peer Instruction (TMPI), whereby students are prompted to submit explanations to justify their choice in a multiple-choice question (MCQ), and are subsequently presented with explanations written by their peers, after which they can reconsider their own answer. TMPI allows students to contrast their reasoning with a variety of peer-submitted explanations. It is intended to foster reflection, ultimately leading to better learning. However, not all content submitted by students is adequate and it must be curated, a process that can require a significant effort by the teacher. The curation process ought to be automated for learnersourcing in TMPI to scale up to large classes, such as MOOCs. Even for smaller settings, automation is critical for the timely curation of student-submitted content, such as within a single assignment, or during a semester. We adapt methods from argument mining and natural language processing to address the curation challenge and assess the quality of student answers submitted in TMPI, as judged by their peers. The curation task is confined to the prediction of argument convincingness: an explanation submitted by a learner is considered of good quality, if it is convincing to their peers. We define a methodology to measure convincingness scores using three methods, Bradley-Terry, Crowd-Bradley-Terry and WinRate. We assess the performance of feature-rich supervised learning algorithms as well as transformer-based neural approach to predict convincingness using these scores. Experiments are conducted over different domains, from ethics to STEM. While the neural approach shows the greatest correlation between its prediction and the different convincingness measures, results show that success on this task is highly dependent on the domain and the type of question. [ABSTRACT FROM AUTHOR]

Published
2023

45. Bayesian Student Models Based on Item to Item Knowledge Structures

Author

Desmarais, Michel C., Gagnon, Michel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Nejdl, Wolfgang, editor, and Tochtermann, Klaus, editor

Published
2006
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46. Computer Adaptive Testing: Comparison of a Probabilistic Network Approach with Item Response Theory

Author

Desmarais, Michel C., Pu, Xiaoming, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Ardissono, Liliana, editor, Brna, Paul, editor, and Mitrovic, Antonija, editor

Published
2005
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