Open Learning Environments, Foundations, Methods, and Models

Learning And Instruction Theory Into Practice

The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must consider a broad array of linguistic, rhetorical, and contextual features. This study assesses the potential for computational indices to predict human ratings of essay quality. Past studies have demonstrated that linguistic indices related to lexical diversity, word frequency, and syntactic complexity are significant predictors of human judgments of essay quality but that indices of cohesion are not. The present study extends prior work by including a larger data sample and an expanded set of indices to assess new lexical, syntactic, cohesion, rhetorical, and reading ease indices. Three models were assessed. The model reported by McNamara, Crossley, and McCarthy (Written Communication 27:57-86, 2010) including three indices of lexical diversity, word frequency, and syntactic complexity accounted for only 6% of the variance in the larger data set. A regression model including the full set of indices examined in prior studies of writing predicted 38% of the variance in human scores of essay quality with 91% adjacent accuracy (i.e., within 1 point). A regression model that also included new indices related to rhetoric and cohesion predicted 44% of the variance with 94% adjacent accuracy. The new indices increased accuracy but, more importantly, afford the means to provide more meaningful feedback in the context of a writing tutoring system.

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Natural Language Processing in an Intelligent Writing Strategy Tutoring System.

Background: Motivation matters in medical students' academic performance. However, few studies have specifically examined how motivation and external environmental factors (e.g., institutions) affect medical students' performance with large-scale data sets. The roles of self-efficacy and learning engagement in the mechanisms that govern how motivation affects academic performance are still unclear.Objective: This study aims to advance a comprehensive understanding about the relationships between medical students' motivation, self-efficacy, learning engagement, and academic performance in a nationwide survey, taking students' demographic factors and sociocultural environments into consideration.Design: We collected data from 1930 medical students in China. We probed the relations between studying variables. We then performed structural equation model (SEM) analysis to examine the mediating roles of self-efficacy and learning engagement on the relationship between motivation and academic performance. We further carried out multiple-group SEM analyses to compare differences between males and females, and between students in key universities and colleges (KUCs) and non-key universities and colleges (NKUCs).Results: Medical students in KUCs demonstrated significantly higher intrinsic motivation, better academic performance and lower extrinsic motivation than those in NKUCs. Male students reported higher intrinsic motivation but surprisingly lower academic performance than females. The total effect of intrinsic motivation on academic performance was larger than that of extrinsic motivation. There were significant indirect effects of either intrinsic or extrinsic motivation on academic performance through learning engagement. Besides, both intrinsic motivation and extrinsic motivation predicted self-efficacy; however, the direct effect of self-efficacy on academic performance was not significant.Conclusions: This study provided researchers with a holistic picture of students' types of motivation in relation to academic performance. Findings from this study can help in rethinking the role of self-efficacy in medicine, in finding more effective interventions for promoting medical students' levels of motivation, and in developing motivation-related counselling methods for different groups of medical students.

https://www.tandfonline.com/doi/pdf/10.1080/10872981.2020.1742964

Medical students' motivation and academic performance: the mediating roles of self-efficacy and learning engagement.

Please find following a submission by the One Laptop per Child Foundation to the review on the future direction of Australia's aid program.

One Laptop Per Child

What drives students’ intention to use tablet computers: An extended technology acceptance model

The one-to-one computing environment provides students adaptive and individualized learning experiences, which has the great potential to enhance their learning performances, but it also requires students to actively motivate themselves to self-regulate their learning processes. This study examined the relationships between self-efficacy, task values, and self-regulated learning (SRL) in a one-to-one computing environment. Specifically, how task values (intrinsic value, utility value, and cost) mediate the relationship between students’ self-efficacy and SRL was explored. A total of 299 seventh grade students (176 boys and 123 girls, with an average age of 15) from 8 classrooms of one public school volunteered to participate in this study. They were asked to complete a questionnaire pertaining to their perceptions of task values, self-efficacy, and SRL when using eSchoolbag, a type of one-to-one computing environment, to assist their learning. Results revealed that self-efficacy was a significant predictor of students’ SRL, and their intrinsic value, utility value, and cost. Intrinsic value and utility value both significantly predict students’ SRL. However, the cost of a task did not predict students’ SRL. Utility value significantly mediated the relationship between self-efficacy and SRL, whereas the indirect effect of self-efficacy on SRL mediated by intrinsic value was not significant. The findings from this study provided important practical implications for the teaching and learning in one-to-one computing environment.

The Relationship between Self-Efficacy and Self-Regulated Learning in One-to-One Computing Environment: The Mediated Role of Task Values.

It is vital to develop an understanding of students' self-regulatory processes in the domains of STEM (Science, Technology, Engineering, and Mathematics) for the quality delivery of STEM education. However, most studies have followed a variable-centered approach, leaving open the question of how specific SRL (Self-regulated Learning) behaviors group within individual learners. Furthermore, little is known about how students' SRL profiles unfold over time in STEM education, specifically in the context of engineering design. In this study, we examined the change of students’ SRL profiles over time as 108 middle school students designed green buildings in a simulation-based computer-aided design (CAD) environment. We identified three distinct SRL profiles using a longitudinal clustering approach: reflective-oriented, adaptive, and minimally self-regulated learners. In addition, we found that students with different profile memberships differed significantly in their design performance. Specifically, adaptive self-regulated learners outperformed minimally self-regulated learners on design completeness, whereas reflective-oriented self-regulated learners demonstrated higher design efficiency than minimally self-regulated learners. This study provides researchers with both theoretical and methodological insights concerning SRL dynamics. Findings from this research also inform practitioners about the design of adaptive interventions.

Shan Li

Papers

Medical students' motivation and academic performance: the mediating roles of self-efficacy and learning engagement.

What drives students’ intention to use tablet computers: An extended technology acceptance model

The Relationship between Self-Efficacy and Self-Regulated Learning in One-to-One Computing Environment: The Mediated Role of Task Values.

Longitudinal clustering of students’ self-regulated learning behaviors in engineering design

Examining the interplay of affect and self regulation in the context of clinical reasoning