Corner-fed microstrip antenna analysis for dual polarization     

Mohammed Ahmed ZHONG Shun shi 《上海大学学报(英文版)》1999,3(4):279-281

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

Mohammed Saqr 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1077-1094

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 notes

What 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.

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