Academic motivation-achievement cycle and the behavioural pathways: A short-timeframe experiment with manipulated perceived achievement.

Background: The purported reciprocity between motivation and academic achievement in education has largely been supported by correlational data.

Aims: Our first aim was to determine experimentally whether motivation and achievement are reciprocally related. The second objective was to investigate a potential behavioural mediation pathway between motivation and achievement by measuring the objective effort expended on learning.

Challenge or threat? A Q-methodological study into nursing students' perceptions on learning to collaborate under stress.

Introduction: Nursing students will come across stressful situations during their internships and will continue to do so in future practice. Because of the impact stress can have on performance, nursing students need to be equipped to work and collaborate in such situations. Careful consideration of the needs and desires of nursing students should be taken in account, in order to create a training environment that fosters students' ability to learn to collaborate under stress.

Correction: Liu et al. (2023). Testing the Reciprocal Effect between Value of Education, Time Investment, and Academic Achievement in a Large Non-Western Sample. 11: 1333.

In a recent paper, while our overall conclusion remains valid, one specific finding-that there are reciprocal relationships between how Korean students value education, the time they invest in their studies, and their academic achievements-requires correction (Liu et al [...

Language models outperform cloze predictability in a cognitive model of reading.

Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. This study investigates whether large language models (LLMs) can fill this gap.