Modelling within‐person idiographic variance could help explain and individualize learning

Abstract

Learning analytics is a fast‐growing discipline. Institutions and countries alike are racing to harness the power of using data to support students, teachers and stakeholders. Research in the field has proven that predicting and supporting underachieving students is worthwhile. Nonetheless, challenges remain unresolved, for example, lack of generalizability, portability and failure to advance our understanding of students' behaviour. Recently, interest has grown in modelling individual or within‐person behaviour, that is, understanding the person‐specific changes. This study applies a novel method that combines within‐person with between‐person variance to better understand how changes unfolding at the individual level can explain students' final grades. By modelling the within‐person variance, we directly model where the process takes place, that is the student. Our study finds that combining within‐ and between‐person variance offers a better explanatory power and a better guidance of the variables that could be targeted for intervention at the personal and group levels. Furthermore, using within‐person variance opens the door for person‐specific idiographic models that work on individual student data and offer students support based on their own insights. Practitioner notesWhat is already known about this topic Predicting students' performance has commonly been implemented using cross‐sectional data at the group level. Predictive models help predict and explain student performance in individual courses but are hard to generalize. Heterogeneity has been a major factor in hindering cross‐course or context generalization. What this paper adds Intra‐individual (within‐person) variations can be modelled using repeated measures data. Hybrid between–within‐person models offer more explanatory and predictive power of students' performance. Intra‐individual variations do not mirror interindividual variations, and thus, generalization is not warranted. Regularity is a robust predictor of student performance at both the individual and the group levels. Implications for practice The study offers a method for teachers to better understand and predict students' performance. The study offers a method of identifying what works on a group or personal level. Intervention at the personal level can be more effective when using within‐person predictors and at the group level when using between‐person predictors.

Investigating the reliability of aggregate measurements of learning process data: From theory to practice

“…The reliability of aggregate measurements may increase as the period becomes longer (in a more general term, the analytic unit or granularity becomes coarser), given that longer periods contain more events indicative of the same construct of interest, and the construct is stable within the period. However, when the period is relatively long, changes may occur in learning behaviours, which represents temporal within-subject variation (Saqr, 2023). Such variation can be useful for understanding behavioural adaptation (Williams et al, 2021).…”

Advancing theory in the age of artificial intelligence

“…The paper by Saqr (2023) tackles an existing challenge for AI and LA research. While the development of predictive models of academic performance is now a relatively common practice, demonstrating improved change remains elusive.…”

Mapping the self in self‐regulation using complex dynamic systems approach

“…Yet, group‐based methods suffer several issues that make their representations of individualized mechanisms concerning (Beck & Jackson, 2020, 2021; Fisher et al., 2018; Molenaar, 2004; Saqr, 2023b). First, in group‐based methods, the central tendency (mean or median) is commonly calculated as a ‘norm’ or yardstick, which most people are assumed to align to Figure 2a.…”

“…Contrary to group‐based methods, idiographic (person‐specific) methods accurately and precisely model the individual person, create person‐specific models and devise unique parameters for each individual (Figure 2b). Modelling the person—where the process takes place—paves the way for accurate, personalized and adaptive education (Malmberg, Saqr, Järvenoja, & Järvelä, 2022; Saqr, 2023b; Wright & Zimmermann, 2019). As illustrated in Figure 1b, a process is measured repeatedly within the same person to capture the intra‐individual variance across time.…”