Assisting instructors in the design and provision of personalised la-informed feedback in moocs

Resumen

Massive Open Online Courses (MOOCs) have gained increasing prominence in the educational landscape over the last decade. Despite their educational benefits and global adoption, MOOCs are still accompanied by several challenges that have an impact on the learning experience. The provision of timely and personalised feedback is one important challenge, often associated with learner disengagement. The field of Learning Analytics (LA) provides opportunities for scaling up the feedback interventions, enabling automated or semi-automated interventions. Yet, these LA solutions often lack course contextualisation, pedagogical grounding, and guidance for MOOC instructors in understanding and using such feedback tools. Building on this context, the current dissertation aims to assist instructors in the design and provision of personalised LA-informed feedback in MOOC environments. To this end, this dissertation proposes the accomplishment of three research objectives following a Design-Based Research methodological approach. This methodological approach resulted into four cycles, in which we employed a human-centred approach, involving MOOC instructors (and other stakeholders) both in the identification of the research problems, and in the design and refinement of the suggested proposals. The first research objective deals with understanding the current state of instructor-led LA-informed feedback in MOOCs. Accordingly, we conducted a systematic literature review to understand the current LA proposals that support personalised interventions in MOOCs, and their implications for learners and instructors. The second research objective delves into the need of helping MOOC instructors to shape personalised and contextualised feedback. In response to this need, this dissertation proposes a conceptual framework named FeeD4Mi. FeeD4Mi includes a 5-dimension conceptual structure, accompanied by a process, a set of catalogues and a set of recommendations based on the identified learners’ problems for their courses. The third research objective aims at providing a manageable design and provision of personalised and contextualised feedback strategies in MOOCs. To this end, this dissertation proposes a set of design guidelines that can be transformed in a web-based tool to incorporate the FeeD4Mi catalogues, process and recommendations in a digital format, enabling the computer-interpretable representation of feedback strategies in MOOCs and their automatic or semi-automatic implementation during the enactment of MOOCs. In total, four evaluative studies served to iteratively refine and assess FeeD4Mi regarding its completeness, usefulness for MOOC instructors, and impact of the feedback strategies designed on learners and instructors. At the same time, we assessed with MOOC instructors the usability, temporal workload, and potential adoption of the digital version of the framework. The results indicated the added value of the framework in guiding instructors in the design and provision of LA-informed feedback in MOOCs. Furthermore, the evaluators highlighted the flexibility of the tool, the possibility to automate feedback strategies and the usefulness in reflecting on potential learners’ problems, LA-based indicators and feedback reactions. Finally, the results of the evaluative studies also pointed out further research directions of feedback MOOCs and in other educational contexts where delivering feedback is also challenging (e.g., online, or hybrid teaching).