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Paassen, Benjamin; McBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – Journal of Educational Data Mining, 2021
Educational data mining involves the application of data mining techniques to student activity. However, in the context of computer programming, many data mining techniques can not be applied because they require vector-shaped input, whereas computer programs have the form of syntax trees. In this paper, we present ast2vec, a neural network that…
Descriptors: Data Analysis, Programming Languages, Networks, Novices
Picones, Gio; PaaBen, Benjamin; Koprinska, Irena; Yacef, Kalina – International Educational Data Mining Society, 2022
In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a…
Descriptors: Prediction, Academic Achievement, Learning Analytics, Concept Mapping
Lockwood, Elise – Cognition and Instruction, 2022
In this paper, I discuss undergraduate students' engagement in basic Python programming while solving combinatorial problems. Students solved tasks that were designed to involve programming, and they were encouraged to engage in activities of prediction and reflection. I provide data from two paired teaching experiments, and I outline how the task…
Descriptors: Undergraduate Students, Thinking Skills, Prediction, Teaching Methods
Orr, J. Walker; Russell, Nathaniel – International Educational Data Mining Society, 2021
The assessment of program functionality can generally be accomplished with straight-forward unit tests. However, assessing the design quality of a program is a much more difficult and nuanced problem. Design quality is an important consideration since it affects the readability and maintainability of programs. Assessing design quality and giving…
Descriptors: Programming Languages, Feedback (Response), Units of Study, Computer Science Education
Jimenez, Fernando; Paoletti, Alessia; Sanchez, Gracia; Sciavicco, Guido – IEEE Transactions on Learning Technologies, 2019
In the European academic systems, the public funding to single universities depends on many factors, which are periodically evaluated. One of such factors is the rate of success, that is, the rate of students that do complete their course of study. At many levels, therefore, there is an increasing interest in being able to predict the risk that a…
Descriptors: Prediction, Risk, Dropouts, College Students
Iatrellis, Omiros; Savvas, Ilias ?.; Fitsilis, Panos; Gerogiannis, Vassilis C. – Education and Information Technologies, 2021
Learning analytics have proved promising capabilities and opportunities to many aspects of academic research and higher education studies. Data-driven insights can significantly contribute to provide solutions for curbing costs and improving education quality. This paper adopts a two-phase machine learning approach, which utilizes both…
Descriptors: Prediction, Outcomes of Education, Higher Education, Data Analysis
Liu, Xiaoming; Schwieger, Dana – Information Systems Education Journal, 2023
Rapid advancements and emergent technologies add an additional layer of complexity to preparing computer science and information technology higher education students for entering the post pandemic job market. Knowing and predicting employers' technical skill needs is essential for shaping curriculum development to address the emergent skill gap.…
Descriptors: Network Analysis, Employment Opportunities, Information Technology, Computer Science Education
Sangal, Somya; Kataria, Shreya; Tyagi, Twishi; Gupta, Nidhi; Kirtani, Yukti; Agrawal, Shivli; Chakraborty, Pinaki – Education and Information Technologies, 2018
A parsing algorithm visualizer is a tool that visualizes the construction of a parser for a given context-free grammar and then illustrates the use of that parser to parse a given string. Parsing algorithm visualizers are used to teach the course on compiler construction which in invariably included in all undergraduate computer science curricula.…
Descriptors: Teaching Methods, Computer Software, Undergraduate Students, Computer Science Education
Saito, Tomohiro; Watanobe, Yutaka – International Journal of Distance Education Technologies, 2020
Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to…
Descriptors: Programming, Computer Science Education, Electronic Learning, Instructional Materials
Price, Thomas; Zhi, Rui; Barnes, Tiffany – International Educational Data Mining Society, 2017
In this paper we present a novel, data-driven algorithm for generating feedback for students on open-ended programming problems. The feedback goes beyond next-step hints, annotating a student's whole program with suggested edits, including code that should be moved or reordered. We also build on existing work to design a methodology for evaluating…
Descriptors: Feedback (Response), Computer Software, Data Analysis, Programming
Wilson, Kimberly; Narayan, Anupama – Educational Psychology, 2016
This study investigates relationships between self-efficacy, self-regulated learning strategy use and academic performance. Participants were 96 undergraduate students working on projects with three subtasks (idea generation task, methodical task and data collection) in a blended learning environment. Task self-efficacy was measured with…
Descriptors: Self Efficacy, Learning Strategies, Blended Learning, Correlation
Krishnamurthy, Janaki – ProQuest LLC, 2010
Given the increasing competition in the software industry and the critical consequences of software errors, it has become important for companies to achieve high levels of software quality. While cost reduction and timeliness of projects continue to be important measures, software companies are placing increasing attention on identifying the user…
Descriptors: Program Evaluation, Opinions, Privacy, Prediction
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing

Gray, John S. – Journal of Computing in Higher Education, 1994
A detailed analysis and computer-based solution to a puzzle addressing the arrangement of dominoes on a grid is presented. The problem is one used in a college-level data structures or algorithms course. The solution uses backtracking to generate all possible answers. Details of the use of backtracking and techniques for mapping abstract problems…
Descriptors: Algorithms, Computer Assisted Instruction, Computer Science Education, Computer Software
Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John – International Working Group on Educational Data Mining, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…
Descriptors: Academic Achievement, Logical Thinking, Profiles, Tutoring
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