Showing papers by "Dirk T. Tempelaar published in 2016"

What learning analytics based prediction models tell us about feedback preferences of students

Abstract: Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners.

Expertise Development of Hospitality Students: Do Personality, Emotional Intelligence and Learning Style Matter?

Abstract: Personality, emotional intelligence, and learning style are generally considered important success factors and career determinants in hospitality management. This study assesses the influence of these antecedents on the development of expertise in hospitality management. Correlational, ANOVA and hierarchical regression analyses demonstrated that (a) There is limited growth on cognitive performance measures while students are in school, however performance increases after a period in the workplace; (b) Personality, learning style and emotional intelligence showed zero to low correlations with cognitive performance; and (c) Extroversion, conscientiousness, emotional management and theorist learning style showed moderate positive correlations with several performance measures. The findings are partially in alignment with previous studies showing that personality, emotional intelligence and learning style play a role in expertise development in hospitality. It might be concluded that personality, emotional intelligence and learning style do seem important, they are not as important as has been argued in previous research.

Learning analytics to understand cultural impacts on technology enhanced learning

Abstract: In this empirical study, we investigate the role of national cultural dimensions as distal antecedents of the use intensity of e-tutorials, which constitute the digital component within a blended learning course. Profiting from the context of a dispositional learning analytics application, we investigate cognitive processing strategies and metacognitive regulation strategies, motivation and engagement variables, and learning emotions as proximal antecedents of tool use and tool performance. We find that cultural diversity explains a substantial part of the variation in learning dispositions and tool use. The design of personalized learning paths will, therefore, profit from including national cultural dimensions as a relevant design factor.

Verifying the Stability and Sensitivity of Learning Analytics Based Prediction Models: An Extended Case Study

Abstract: In this empirical contribution, a follow-up study of previous research [1], we focus on the issues of stability and sensitivity of Learning Analytics based prediction models. Do predictions models stay intact, when the instructional context is repeated in a new cohort of students, and do predictions models indeed change, when relevant aspects of the instructional context are adapted? Applying Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics combined with formative assessments and learning management systems, we compare two cohorts of a large module introducing mathematics and statistics. Both modules were based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials, and have similar instructional design, except for an intervention into the design of quizzes administered in the module. We analyse bivariate and multivariate relationships of module performance and track and disposition data to provide evidence of both stability and sensitivity of prediction models.

Writing Analytics for Epistemic Features of Student Writing

Abstract: Literacy, encompassing the ability to produce written outputs from the reading of multiple sources, is a key learning goal. Selecting information, and evaluating and integrating claims from potentially competing documents is a complex literacy task. Prior research exploring differing behaviours and their association to constructs such as epistemic cognition has used ‘multiple document processing’ (MDP) tasks. Using this model, 270 paired participants, wrote a review of a document. Reports were assessed using a rubric associated with features of complex literacy behaviours. This paper focuses on the conceptual and empirical associations between those rubric-marks and textual features of the reports on a set of natural language processing (NLP) indicators. Findings indicate the potential of NLP indicators for providing feedback regarding the writing of such outputs, demonstrating clear relationships both across rubric facets and between rubric facets and specific NLP indicators.