Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education |
| Number of pages | 25 |
| Place of Publication | New York, NY, USA |
| Publisher | ACM |
| Publication date | 2018 |
| Pages | 175-199 |
| ISBN (Print) | 978-1-4503-6223-8 |
| DOIs | |
| Publication status | Published - 2018 |
| MoE publication type | A4 Article in conference proceedings |
| Event | 23rd Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE 2018) - University of Central Lancashire, Cyprus, Larnaca, Cyprus Duration: 2 Jul 2018 → 4 Jul 2018 Conference number: 23 https://iticse.acm.org/ |
Publication series
| Name | ITiCSE 2018 Companion |
|---|---|
| Publisher | ACM |
Fields of Science
- analytics
- educational data mining
- learning analytics
- literature review
- mapping study
- performance
- prediction
- 113 Computer and information sciences
Cite this
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Predicting Academic Performance : A Systematic Literature Review. / Hellas, Arto; Ihantola, Petri; Petersen, Andrew; Ajanovski, Vangel V.; Gutica, Mirela; Hynninen, Timo; Knutas, Antti; Leinonen, Juho; Messom, Chris; Liao, Soohyun Nam.
Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education . New York, NY, USA : ACM, 2018. p. 175-199 (ITiCSE 2018 Companion).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
TY - GEN
T1 - Predicting Academic Performance
T2 - A Systematic Literature Review
AU - Hellas, Arto
AU - Ihantola, Petri
AU - Petersen, Andrew
AU - Ajanovski, Vangel V.
AU - Gutica, Mirela
AU - Hynninen, Timo
AU - Knutas, Antti
AU - Leinonen, Juho
AU - Messom, Chris
AU - Liao, Soohyun Nam
PY - 2018
Y1 - 2018
N2 - The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.
AB - The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.
KW - analytics
KW - educational data mining
KW - learning analytics
KW - literature review
KW - mapping study
KW - performance
KW - prediction
KW - 113 Computer and information sciences
U2 - 10.1145/3293881.3295783
DO - 10.1145/3293881.3295783
M3 - Conference contribution
SN - 978-1-4503-6223-8
T3 - ITiCSE 2018 Companion
SP - 175
EP - 199
BT - Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education
PB - ACM
CY - New York, NY, USA
ER -