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Showing 1 to 15 of 33 results Save | Export
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Zhang, Yingbin; Pinto, Juan D.; Fan, Aysa Xuemo; Paquette, Luc – Journal of Educational Data Mining, 2023
The second CSEDM data challenge aimed at finding innovative methods to use students' programming traces to model their learning. The main challenge of this task is how to decide which past problems are relevant for predicting performance on a future problem. This paper proposes a set of weighting schemes to address this challenge. Specifically,…
Descriptors: Problem Solving, Introductory Courses, Computer Science Education, Programming
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Robbert Smit; Rahel Schmid; Nicolas Robin – British Journal of Educational Technology, 2025
Secondary school students (N = 269) participated in a daylong visual programming course held in a stimulating environment for start-up enterprises. The tasks were application-oriented and partly creative. For example, a wearable device with light-emitting diodes, (ie, LEDs) could be applied to a T-shirt and used for optical messages. Our research…
Descriptors: Self Efficacy, Gender Differences, Prediction, Student Attitudes
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Veerasamy, Ashok Kumar; Laakso, Mikko-Jussi; D'Souza, Daryl – Informatics in Education, 2022
Previous studies have proposed many indicators to assess the effect of student engagement in learning and academic achievement but have not yet been clearly articulated. In addition, while student engagement tracking systems have been designed, they rely on the log data but not on performance data. This paper presents results of a non-machine…
Descriptors: Formative Evaluation, Educational Indicators, Learner Engagement, At Risk Students
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Tanaka, Tetsuo; Ueda, Mari – International Association for Development of the Information Society, 2023
In this study, the authors have developed a web-based programming exercise system currently implemented in classrooms. This system not only provides students with a web-based programming environment but also tracks the time spent on exercises, logging operations such as program editing, building, execution, and testing. Additionally, it records…
Descriptors: Scores, Prediction, Programming, Artificial Intelligence
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David Roldan-Alvarez; Francisco J. Mesa – IEEE Transactions on Education, 2024
Artificial intelligence (AI) in programming teaching is something that still has to be explored, since in this area assessment tools that allow grading the students work are the most common ones, but there are not many tools aimed toward providing feedback to the students in the process of creating their program. In this work a small sized…
Descriptors: Intelligent Tutoring Systems, Grading, Artificial Intelligence, Feedback (Response)
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Van Petegem, Charlotte; Deconinck, Louise; Mourisse, Dieter; Maertens, Rien; Strijbol, Niko; Dhoedt, Bart; De Wever, Bram; Dawyndt, Peter; Mesuere, Bart – Journal of Educational Computing Research, 2023
We present a privacy-friendly early-detection framework to identify students at risk of failing in introductory programming courses at university. The framework was validated for two different courses with annual editions taken by higher education students (N = 2 080) and was found to be highly accurate and robust against variation in course…
Descriptors: Pass Fail Grading, At Risk Students, Introductory Courses, Programming
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Denis Zhidkikh; Ville Heilala; Charlotte Van Petegem; Peter Dawyndt; Miitta Jarvinen; Sami Viitanen; Bram De Wever; Bart Mesuere; Vesa Lappalainen; Lauri Kettunen; Raija Hämäläinen – Journal of Learning Analytics, 2024
Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first…
Descriptors: Learning Analytics, Prediction, School Holding Power, Academic Achievement
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Zhang, Jingjing; Huang, Yicheng; Gao, Ming – Journal of Learning Analytics, 2022
Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible…
Descriptors: Network Analysis, Video Technology, MOOCs, Attention Control
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Xu, Jia; Wei, Tingting; Lv, Pin – International Educational Data Mining Society, 2022
In an Intelligent Tutoring System (ITS), problem (or question) difficulty is one of the most critical parameters, directly impacting problem design, test paper organization, result analysis, and even the fairness guarantee. However, it is very difficult to evaluate the problem difficulty by organized pre-tests or by expertise, because these…
Descriptors: Prediction, Programming, Natural Language Processing, Databases
Trenton W. Dawson – ProQuest LLC, 2022
This study aimed to understand the identity and attitude of students enrolled in computer science (CS) or programming-related course at community colleges nationwide. This study quantitatively evaluation data for estimating the relationships between students' identity and attitudes toward computer science with prior programming experience and…
Descriptors: Computer Science Education, Trend Analysis, Programming, Community College Students
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Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2022
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In…
Descriptors: Programming, Knowledge Level, Prediction, Instructional Innovation
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Singla, Adish; Theodoropoulos, Nikitas – International Educational Data Mining Society, 2022
Block-based visual programming environments are increasingly used to introduce computing concepts to beginners. Given that programming tasks are open-ended and conceptual, novice students often struggle when learning in these environments. AI-driven programming tutors hold great promise in automatically assisting struggling students, and need…
Descriptors: Programming, Computer Science Education, Task Analysis, Introductory Courses
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Umar Shehzad; Jody Clarke-Midura; Mimi Recker – ACM Transactions on Computing Education, 2024
Objectives: The increasing demand for computing skills has led to a rapid rise in the development of new computer science (CS) curricula, many with the goal of equitably broadening the participation of underrepresented students in CS. While such initiatives are vital, factors outside of the school environment also play a role in influencing…
Descriptors: Parent Child Relationship, Computer Science Education, Programming, Equal Education
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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
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Ma, Yingbo; Katuka, Gloria Ashiya; Celepkolu, Mehmet; Boyer, Kristy Elizabeth – International Educational Data Mining Society, 2022
Collaborative learning is a complex process during which two or more learners exchange opinions, construct shared knowledge, and solve problems together. While engaging in this interactive process, learners' satisfaction toward their partners plays a crucial role in defining the success of the collaboration. If intelligent systems could predict…
Descriptors: Middle School Students, Cooperative Learning, Prediction, Peer Relationship
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