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Direct linkJun Rao – ProQuest LLC, 2021
In recent years, not only has there been a dramatic drop in the number of students enrolling in computer science courses, and attrition from computer science courses continues to be significant. Traditionally, computer programming courses have high failure rates, and as they tend to be core to computer science courses can be a roadblock for many…
Descriptors: Self Efficacy, Student Evaluation, Grading, Computer Science Education
Peer reviewed
Download full textAyub, Mewati; Karnalim, Oscar; Risal, Risal; Senjaya, Wenny Franciska; Wijanto, Maresha Caroline – Journal of Technology and Science Education, 2019
Due to its high failure rate, Introductory Programming has become a main concern. One of the main issues is the incapability of slow-paced students to cope up with given programming materials. This paper proposes a learning technique which utilises pair programming to help slow-paced students on Introductory Programming; each slow-paced student is…
Descriptors: Introductory Courses, Computer Science Education, Teaching Methods, Programming
McBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – International Educational Data Mining Society, 2016
Effective mining of data from online submission systems offers the potential to improve educational outcomes by identifying student habits and behaviours and their relationship with levels of achievement. In particular, it may assist in identifying students at risk of performing poorly, allowing for early intervention. In this paper we investigate…
Descriptors: Data Collection, Student Behavior, Academic Achievement, Correlation
Azevedo, Ana, Ed.; Azevedo, José, Ed. – IGI Global, 2019
E-assessments of students profoundly influence their motivation and play a key role in the educational process. Adapting assessment techniques to current technological advancements allows for effective pedagogical practices, learning processes, and student engagement. The "Handbook of Research on E-Assessment in Higher Education"…
Descriptors: Higher Education, Computer Assisted Testing, Multiple Choice Tests, Guides
Chen, Yao-Hsien; Cheng, Ching-Hsue; Liu, Jing-Wei – Computers & Education, 2010
In order to evaluate student learning achievement, several aspects should be considered, such as exercises, examinations, and observations. Traditionally, such an evaluation calculates a final score using a weighted average method after awarding numerical scores, and then determines a grade according to a set of established crisp criteria.…
Descriptors: Feedback (Response), Academic Achievement, Student Evaluation, Grading
Malmi, Lauri; Karavirta, Ville; Korhonen, Ari; Nikander, Jussi – Journal on Educational Resources in Computing, 2005
In this paper, we present our experiences in using two automatic assessment tools, TRAKLA and TRAKLA2, in a second course of programming. In this course, 500-700 students have been enrolled annually during the period 1993-2004. The tools are specifically designed for assessing algorithm simulation exercises in which students simulate the working…
Descriptors: Feedback (Response), Evaluation, Grading, Mathematics
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection
