Predicting Mathematical Performance
The Effect of Cognitive Processes and Self-Regulation Factors
Bibliographic Data
| ID | 5686464 |
|---|---|
| Authors | Mariel Musso (0000-0002-3226-5076), Eva Kyndt (0000-0002-6755-4409), Eduardo Cascallar (0000-0003-2537-3391), Filip Dochy |
| Year | 2012 |
| Volume | 2012 |
| Pages | 1-13 |
| Publication date | 2012-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Research International (JOURNAL) |
| Journal identifiers | ISSN: 2090-4010 • E-ISSN: 2090-4002 |
| Publisher | Hindawi Limited (PUBLISHER) |
| DOI | 10.1155/2012/250719 |
| OpenAlex | W1964821885 |
| Language | EN |
| Citations received | 7 |
| References cited | 55 |
A substantial number of research studies have investigated the separate influence of working memory, attention, motivation, and learning strategies on mathematical performance and self-regulation in general. There is still little understanding of their impact on performance when taken together, understanding their interactions, and how much each of them contributes to the prediction of mathematical performance. With the emergence of new methodologies and technologies, such as the modelling with predictive systems, it is now possible to study these effects with approaches which use a wide range of data, including student characteristics, to estimate future performance without the need of traditional testing (Boekaerts and Cascallar, 2006). This research examines the different cognitive patterns and complex relations between cognitive variables, motivation, and background variables associated with different levels of mathematical performance using artificial neural networks (ANNs). A sample of 800 entering university students was used to develop three ANN models to identify the expected future level of performance in a mathematics test. These ANN models achieved high degree of precision in the correct classification of future levels of performance, showing differences in the pattern of relative predictive weight amongst those variables. The impact on educational quality, improvement, and accountability is highlighted
Accountability · Artificial neural network · Cognition · Cognitive psychology · Machine learning · Quality (philosophy · Range (aeronautics · Sample (material · Cognitive Science and Mapping · Computer Science · Engineering · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Psychology · Artificial Intelligence
Socio-economic status and academic performance in higher education
Predicting attribution of letter writing performance in secondary school
Identifying Reliable Predictors of Educational Outcomes Through Machine-Learning Predictive Modeling
Predicting key educational outcomes in academic trajectories
Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy
A Framework for Detecting Factors Influencing Students’ Academic Performance
Structural Model of Students' Interest and Self-Motivation to Learning Mathematics
The Role of Epistemic Beliefs in Self-Regulated Learning
Learning representations by back-propagating errors
An automated version of the operation span task
Self-regulated Learning at the Junction of Cognition and Motivation
How Far Have We Moved Toward the Integration of Theory and Practice in Self-Regulation?
Attention, self–regulation and consciousness
The separability of working memory resources for spatial thinking and language processing
Testing the Efficiency and Independence of Attentional Networks
Individual differences in working memory and reading
Working Memory Capacity as Executive Attention
Predicting Graduate Student Success in an MBA Program
Predicting item difficulty in a reading comprehension test with an artificial neural network
Why do adult age differences increase with task complexity
Decomposing adult age differences in working memory
A theory of motivation for some classroom experiences
Working memory deficits in children with low achievements in the national curriculum at 7 years of age
Mathematical disabilities
| Unique citing works | 7 |
|---|---|
| Citations per year | 0,88 |
| Citation span | 2018 - 2023 (6) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 7 |