ABSTRACT
The COVID-19 pandemic has caused teachers across India to use Edtech products in teaching. But teachers face multiple challenges, ranging from selecting appropriate Edtech tools to developing their own teaching practice for teaching with Edtech. The currently available taxonomies and landscapes of Edtech products either address student-facing products or focus on a niche category of teacher-facing products like assessment tools or open-source tools. In this paper, we present a taxonomy of teacher-facing Edtech products that contains a hierarchy of three levels with learner-centric activity tools and teacher professional development forming the base layer. Each category further spins off into multiple subcategories based on various teacher objectives like products for conducting learner-centric activities, generating summative assessments, or developing their teaching competencies. This taxonomy emerged from a systematic literature review and a field-driven affordance analysis of a representative set of eighty products. Our analysis showed that the product landscape was skewed towards learner-centric activity tools (70% of the 80 Edtech products analyzed), revealing the need for more products that support teachers' professional development (TPD). This taxonomy informs teachers about the products available under different Edtech categories. It also includes an affordance analysis that provides additional information about the affordances typical to that particular sub-category.
ABSTRACT
Discussion forum is an integral part of many MOOCs as it provides a platform for peer interaction among learners. The quality of peer interaction is an indicator of the potential for peer learning. Thus, quality of peer interaction provides instructors with an actionable insight into the extent of critical or higher level thinking that learners are engaged in and is a measure of the learning effectiveness of the course. It is daunting for instructors to manually analyze the forum messages to gain this insight. To address this issue, we attempted to develop a system for automatic classification of forum messages that will inform instructors on the quality of peer interaction happening in the forum. Our system classifies messages into predefined classes based on the Interaction Analysis Model phases. We explored and implemented multiple machine learning models. A general accuracy of 95%-97% was observed among the models and no model outperformed the other by a great margin. The needfor such a system has become all the more relevant in the current Covid-19 pandemic situation, where all physical classrooms have had to migrate to an online setting. © 2021 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings. All rights reserved