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Supporting Learning Analytics Adoption

Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.

Cluttering identified: differential diagnostics between cluttering, stuttering and speech impairment related to learning difficulties

Students’ time spent on learning, study strategies and learning outcomes

This study examines the relationships between students’ perceptions of heavy study load, time spent on learning, study strategies, and learning outcomes. Student’s study strategies were measured with a short version of Vermunt’s Inventory of Learning Styles. It was possible to replicate 5 processing and 5 regulation strategies. The higher order dimensions meaning directed learning style (relate and structure, concrete processing, critical processing) and reproduction directed learning style (memorize and repeat, analyze, self-regulation of contents, process and results, external regulation of the learning process) differed from Vermunt. The scales showed differences across groups, which is in line with previous research. Linear structural analysis showed that reproduction directed learning precedes meaning directed learning. Only meaning directed learning affected GPA, the influence of the two learning styles on ECs was not evidenced in this study. Contact hours influenced ECs, but this effect was tempered through its negative association with a heavy study load. The limitations, implications for practice, and directions for further research and development will be discussed in the round table.

Empirical Investigation of Values Affected by AI Systems for Fire Services

Fire fighters operate in a dangerous, dynamic, and complex environment. Artificial Intelligence (AI) systems can contribute to improve fire fighters’ situation awareness and decision-making. However, the introduction of AI systems needs to be done responsibly, taking (human) values into account, especially as the situation in which fire fighters operate is uncertain and decisions have a big impact. In this research, we investigate values that are affected by the introduction of AI systems for fire services by conducting several semi-structured focus group sessions with (operational) fire service personnel. The focus group outcomes are qualitatively analyzed and key values are identified and discussed. This research is a first step in an iterative process towards a generic framework of ethical aspects for the introduction of AI systems in first response, which will give insight into the relevant ethical aspects to take into account when developing AI systems for first responders.

Preparing undergraduate students for lifelong learning

This thesis presents an exploration of ‘how entrepreneurship education pedagogy can enhance undergraduate business students’ autonomous motivation for self-directed learning’. It has twin, equally valuable, purposes: to make an original theoretical contribution and to improve professional practice in this area. The work addresses the lack of pedagogical research in entrepreneurship education that focuses on learner development, with a specific aim at development of self-directed learning skills for lifelong learning. The research is approached with a concurrent, mixed methods design, comparing pre- and a post-EE, self-assessment survey results from 245 students, enrolled in a Young Enterprise venture creation programme, and a control group at a Dutch university. With the use of open-question surveys among the same population, during and after the EE modules, as well as from focus group discussions with a selection of participating students and teachers, explanation was sought for the observations drawn from the quantitative study. Significant relationships were found between students’ self-reported maturity of autonomy, self-efficacy, and motivation for learning, and in how these relate to self-directed learning readiness. Entrepreneurship education was found to significantly moderate the relationship between the learning characteristics and self-directed learning, and to strengthen of the students’ perceived readiness for self-directed learning. Explanation for the impact of EE were found to be related to the stage-wise, mixed pedagogy approach to learning, that combines authentic learning with a hierarchical approach to competence development, and supportive team dynamics. The research contributes to practice with a proposed conceptual framework for understanding how to prepare for self-directed learning readiness and a teaching-learning framework for its development in formal educational settings. It contributes to knowledge with its deeper understanding of how students experience learning in EE and how that affects their willingness to pursue learning opportunities.

Taking affective learning in digital education one step further: trainees’ affective characteristics predicting multicontextual pre-training transfer intention.

The past decades have shown an accelerated development of technology-enhanced or digital education. Although an important and recognized precondition for study success, still little attention has been paid to examining how an affective learning climate can be fostered in online training programs. Besides gaining insight into the dynamics of affective learning itself it is of vital importance to know what predicts trainees’ intention to transfer new knowledge and skills to other contexts. The present study investigated the influence of five affective learner characteristics from the transfer literature (learner readiness, motivation to learn, expected positive outcomes, expected negative outcomes, personal capacity) on trainees’ pre-training transfer intention. Participants were 366 adult students enrolled in an online course in information literacy in a distance learning environment. As information literacy is a generic competence, applicable in various contexts, we developed a novel multicontextual transfer perspective and investigated within one single study the influence of the abovementioned variables on pre-training transfer intention for both the students’ Study and Work contexts. The hypothesized model has been tested using structural equation modeling. The results showed that motivation to learn, expected positive personal outcomes, and learner readiness were the strongest predictors. Results also indicated the benefits of gaining pre-training insight into the specific characteristics of multiple transfer contexts, especially when education in generic competences is involved. Instructional designers might enhance study success by taking affective transfer elements and multicontextuality into account when designing digital education.