Abstract
Research conducted using variable-centered methods uses data from a “group of others” to derive generalizable laws. The average is considered a “norm” where everyone is supposed to be homogeneous and to fit the average yardstick. Deviations from the average are viewed as irregularities rather than natural manifestations of individual differences. However, this homogeneity assumption is theoretically and empirically flawed, leading to inaccurate generalizations about students' behavior based on averages. Alternatively, heterogeneity is a more plausible and realistic characteristic of human functioning and behavior. In this paper, we review the limitations of variable-centered methods and introduce—with empirical examples—person-centered and person-specific methods as alternatives. Person-centered methods are designed with the foundational assumption that humans are heterogeneous, and such heterogeneity can be captured with statistical methods into patterns (or clusters). Person-specific (or idiographic) methods aim to accurately and precisely model the individual person (at the resolution of the single subject sample size). The implications of this paradigm shift are significant, with potential benefits including improved research validity, more effective interventions, and a better understanding of individual differences in learning, and, more importantly, personalization that is tethered to personalized analysis. Educational relevance statement Our study presents a primer on the importance of individual differences, heterogeneity and diversity in capturing the unique peculiarities of students. In doing so, we can offer relevant personalized support that is more equitable and individualized.
| Original language | English |
|---|---|
| Article number | 102492 |
| Number of pages | 8 |
| Journal | Learning and Individual Differences |
| Volume | 113 |
| DOIs | |
| Publication status | Published - 2024 |
Keywords
- Learning analytics
- Idiographic
- Person-specific
- Heterogeneity
- Person-centered
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