Predicting Mathematical Performance: The Effect of Cognitive Processes and Self-Regulation Factors

Abstract

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 ). 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.

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

“…Several studies have shown the importance of attention as a predictor of general academic performance (Gsanger, Homack, Siekierski, & Riccio, 2002;Kyndt et al, 2012, submitted;Riccio, Lee, Romine, Cash and Davis, 2002), reading (Landerl, 2010;Lovett, 1979), mathematical performance (Fernandez-Castillo & Gutiérrez-Rojas, 2009;Fletcher, 2005;Musso et al, 2012), and written expression (Reid, 2006). The research on learning disorders has found that attentional problems are negatively associated to academic achievement (Jimmerson, Dubrow, Adam, Gunnar, & Bozoky 2006).…”

Predicting key educational outcomes in academic trajectories: a machine-learning approach

“…Applications of data science in the prediction of certain and complex educational outcomes, combined with machine learning approaches, have increased during the last decade (Abu Naser 2012; Kanakana and Olanrewaju 2011;Musso et al 2012;Ramaswami and Bhaskaran 2010;Zambrano Matamala et al 2011). However, it would be advantageous to extend these applications to a wide range of problems during all of the students' university trajectory, and to different socio-economic contexts such as those in developing countries.…”

“…Machine learning approaches as artificial neural networks (ANN) allow the use of large volumes of data and non-linear relationships between predictors, and they have been shown to be very effective to classify various educational outcomes (e.g., Abu Naser 2012;Ahmad & Shahzadi 2018;Kanakana and Olanrewaju 2011;Musso et al 2012Musso et al , 2013Lau, Sun & Yang 2019). Other advantages of ANN are that they do not require the fulfillment of assumptions of normality, linearity, and completeness (Kent 2009, Garson 1998.…”

“…Obtaining early-warning information on students at risk can help staff plan and implement support, retention strategies, and other pro-active measures to facilitate the attainment of positive educational outcomes of the at-risk student population. Such a program would also benefit all the student population, with more successful allocation of students to advanced or more challenging courses (Kovacic 2010;Musso et al 2012). Thus, the early prediction of key outcomes in a student's academic trajectory such as grade point average (GPA), academic retention, and degree completion would allow targeted intervention programs in higher education.…”

Relation between learning strategies and techniques, anxiety, and psychosomatic symptoms in 8th and 9th grade elementary school students

“…These dysfunctional attributions of students' failures are often accompanied by feelings of guilt, shame, or anger (Weiner, 1985). More recent evidence suggests that, in addition to cognitive abilities, achieving success in learning also requires efficient learning strategies, which include various types of thinking and behaviour that facilitate the acquisition of new information, integration with existing knowledge and successful recollection (Musso, Kyndt, Cascallar, & Dochy, 2012). Learning techniques include an understanding of what was read, the highlighting of text, the use of textbooks and the writing of notes, explaining unknown terms, etc.…”