Search Results (2)

Multimodal Learning Analytics (MMLA) researchers are progressively employing machine learning (ML) techniques to develop predictive models to improve learning and teaching practices. These predictive models are often evaluated for their generalizability using methods from the ML domain, which do not take into account MMLA’s educational nature. Furthermore, there is a lack of systematization in model evaluation in MMLA, which is also reflected in the heterogeneous reporting of the evaluation results. To overcome these issues, this paper proposes an evaluation framework to assess and report the generalizability of ML models in MMLA (EFAR-MMLA). To illustrate the usefulness of EFAR-MMLA, we present a case study with two datasets, each with audio and log data collected from a classroom during a collaborative learning session. In this case study, regression models are developed for collaboration quality and its sub-dimensions, and their generalizability is evaluated and reported. The framework helped us to systematically detect and report that the models achieved better performance when evaluated using hold-out or cross-validation but quickly degraded when evaluated across different student groups and learning contexts. The framework helps to open up a “wicked problem” in MMLA research that remains fuzzy (i.e., the generalizability of ML models), which is critical to both accumulating knowledge in the research community and demonstrating the practical relevance of these techniques. Full article

Efforts to implement the concept of autonomous transport in the shipping industry are currently underway with the introduction of Maritime Autonomous Surface Ship (MASS), which is expected to usher in a new paradigm in maritime trade. However, this requires a stable supply of highly qualified seafarers. Predicting the changes necessary for seafarer education and training in the MASS era is pivotal for the safe and efficient development and operation of autonomous ships. The present study conducted a survey using Q methodology on fourth year students of the Korea Maritime and Ocean University (KMOU), to examine their perceptions towards changes in ship organizations, and the competency of seafarers required in the MASS era. From the analysis, we extracted three unique clusters of cadets’ perceptions towards new competencies with the introduction of MASS: “the traditional seafarers’ centric role retainer”, the “ship organizational structure domain achiever”, and the “new technical competences builder”. The emerging findings can predict the educational needs and new competences of seafarers in the MASS era, as well as support managerial implications. These results are expected to serve in establishing the future direction of seafarer education and training in both private and public organisations. Full article