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Brown, Neil C. C.; Weill-Tessier, Pierre; Sekula, Maksymilian; Costache, Alexandra-Lucia; Kölling, Michael – ACM Transactions on Computing Education, 2023
Objectives: Java is a popular programming language for use in computing education, but it is difficult to get a wide picture of the issues that it presents for novices; most studies look only at the types or frequency of errors. In this observational study, we aim to learn how novices use different features of the Java language. Participants:…
Descriptors: Novices, Programming, Programming Languages, Data
Omer, Uzma; Tehseen, Rabia; Farooq, Muhammad Shoaib; Abid, Adnan – Education and Information Technologies, 2023
Learning analytics (LA) is a significant field of study to examine and identify difficulties the novice programmers face while learning how to program. Despite producing notable research by the community in the specified area, rare work is observed to synthesize these research efforts and discover the dimensions that guide the future research of…
Descriptors: Programming, Learning Analytics, Educational Research, Data
Rotelli, Daniela; Monreale, Anna – Journal of Learning Analytics, 2023
The increased adoption of online learning environments has resulted in the availability of vast amounts of educational log data, which raises questions that could be answered by a thorough and accurate examination of students' online learning behaviours. Event logs describe something that occurred on a platform and provide multiple dimensions that…
Descriptors: Learning Analytics, Learning Management Systems, Time on Task, Student Behavior
Ulitzsch, Esther; He, Qiwei; Pohl, Steffi – Journal of Educational and Behavioral Statistics, 2022
Interactive tasks designed to elicit real-life problem-solving behavior are rapidly becoming more widely used in educational assessment. Incorrect responses to such tasks can occur for a variety of different reasons such as low proficiency levels, low metacognitive strategies, or motivational issues. We demonstrate how behavioral patterns…
Descriptors: Behavior Patterns, Problem Solving, Failure, Adults
Sandra Jo Wilson; Brian Freeman; E. C. Hedberg – Grantee Submission, 2024
As reporting of effect sizes in evaluation studies has proliferated, researchers and consumers of research need tools for interpreting or benchmarking the magnitude of those effect sizes that are relevant to the intervention, target population, and outcome measure being considered. Similarly, researchers planning education studies with social and…
Descriptors: Benchmarking, Effect Size, Meta Analysis, Statistical Analysis
Klint Kanopka – ProQuest LLC, 2023
As online learning platforms and computerized testing become more common, an increasing amount of data are collected about users. These data include, but are not limited to, response time, keystroke logs, and raw text. The desire to observe these features of the response process reflect an underlying interest in the cognitive processes and…
Descriptors: Scores, Computation, Data Interpretation, Behavior Patterns
Yang Jiang; Mo Zhang; Jiangang Hao; Paul Deane; Chen Li – Journal of Educational Measurement, 2024
The emergence of sophisticated AI tools such as ChatGPT, coupled with the transition to remote delivery of educational assessments in the COVID-19 era, has led to increasing concerns about academic integrity and test security. Using AI tools, test takers can produce high-quality texts effortlessly and use them to game assessments. It is thus…
Descriptors: Integrity, Artificial Intelligence, Technology Uses in Education, Ethics
Kaiwen Man – Educational and Psychological Measurement, 2024
In various fields, including college admission, medical board certifications, and military recruitment, high-stakes decisions are frequently made based on scores obtained from large-scale assessments. These decisions necessitate precise and reliable scores that enable valid inferences to be drawn about test-takers. However, the ability of such…
Descriptors: Prior Learning, Testing, Behavior, Artificial Intelligence
Jelena Andelkovic Labrovic; Nikola Petrovic; Jelena Andelkovic; Marija Meršnik – Journal of Computing in Higher Education, 2025
The focus of this study was on identifying patterns of student behavior to support data-informed decision-making which would then improve the learning experience and learning outcomes of online English language courses. Learning analytics approach (or more specifically cluster analysis) was used to identify engagement patterns in online learning.…
Descriptors: Electronic Learning, Online Courses, Behavior Patterns, Student Behavior
Meng Qu – Education and Information Technologies, 2024
This paper introduces a Patron Counting and Analysis (PCA) system that leverages Wi-Fi-connection data to monitor space utilization and analyze visitor patterns in academic libraries. The PCA system offers real-time crowding information to the public and a comprehensive visitor analysis dashboard for library administrators. The system's…
Descriptors: Academic Libraries, Users (Information), Use Studies, Space Utilization
Kohei Nakamura; Manabu Ishihara; Izumi Horikoshi; Hiroaki Ogata – Smart Learning Environments, 2024
Expectations of big data across various fields, including education, are increasing. However, uncovering valuable insights from big data is like locating a needle in a haystack, and it is difficult for teachers to use educational big data on their own. This study aimed to understand changes in student participation rates during classes and…
Descriptors: Foreign Countries, Junior High School Students, Junior High School Teachers, Public Schools
Shi Pu; Yu Yan; Brandon Zhang – Journal of Educational Data Mining, 2024
We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…
Descriptors: Prediction, Success, Data Analysis, Learning Analytics
Araka, Eric; Oboko, Robert; Maina, Elizaphan; Gitonga, Rhoda – International Review of Research in Open and Distributed Learning, 2022
With the increased emphasis on the benefits of self-regulated learning (SRL), it is important to make use of the huge amounts of educational data generated from online learning environments to identify the appropriate educational data mining (EDM) techniques that can help explore and understand online learners' behavioral patterns. Understanding…
Descriptors: Data Analysis, Metacognition, Comparative Analysis, Behavior Patterns
Çebi, Ayça; Araújo, Rafael D.; Brusilovsky, Peter – Journal of Research on Technology in Education, 2023
Online learning systems allow learners to freely access learning contents and record their interactions throughout their engagement with the content. By using data mining techniques on the student log data of those systems, it is possible to examine learning behavior and reveal navigation patterns through learning contents. This study was aimed at…
Descriptors: Individual Characteristics, Electronic Learning, Student Behavior, Learning Management Systems
Mutahar Qassem; Buthainah M. Al Thowaini – Education and Information Technologies, 2024
Leveraging technological tools in educational research expands investigations into participants' behaviors and provides insights into learning patterns and teaching effectiveness through innovative data collection and analysis methods. Using keylogging data software, this study explored trainee translators' process and product behaviors under two…
Descriptors: Journalism, Translation, Keyboarding (Data Entry), Language Processing
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