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Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of…
The Curriculum Recommendations paradigm is dedicated to fostering learning equality within the ever-evolving realms of educational technology and curriculum development. In acknowledging the inherent obstacles posed by existing…
With unprecedented and growing interest in data science education, there are limited educator materials that provide meaningful opportunities for learners to practice statistical thinking, as defined by Wild and Pfannkuch (1999), with messy…
The ability to express scientific concepts in mathematical terms and integrate scientific and mathematical reasoning about a phenomenon is a foundational cognitive process involved in scientific thinking. This process called blended…
Science communication forms the bridge between computer science researchers and their target audience. Researchers who can effectively draw attention to their research findings and communicate them comprehensibly not only help their target…
Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data. However, the learned representations could potentially lead to performance…
I describe an activity created to help our students learn how to write a scientific paper by reverse engineering a contrastive set of existing papers. I look at three recent implementations of this activity and use a multiple cognitive…
The StorySpace project studies the role new interface technologies might play in high school education. With this approach in mind, StorySpace is specifically designed to support and enhance classroom narrative, an already well-established…
The gap between theory and practice is well-documented in educational research. Physics teachers' willingness to apply research findings in practice may be influenced by a sceptical attitude towards science education research. This study…
In an era where learning is considered a problem, we decided to go for problems for the sake of learning! The purpose of this study was to throw light on the issues involved in two forms of PBL viz., Case Study Based PBL and Research Based…
Using 6 years of evaluation data we assess the medium- and long-term impacts upon a diverse range of students, teachers, and schools from participating in a programme of protracted university-mentored projects based in cutting-edge space…
This experimental study investigates the effectiveness of the Cognitive Conflict-Based Generative Learning Model (GLBCC) in enhancing science literacy among high school physics students. The novelty of this research lies in the innovative…
Blended mathematical sensemaking in science (blended MSS) involves deep conceptual understanding of quantitative relationships describing phenomena in science and has been studies in various disciplines. However, no unified characterization…
Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field. It serves applications in the domains of healthcare, commerce, education, and so on. Particularly, NLP has been widely applied to…
The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to learn a large number of diverse behaviors, or growing-size…
This paper investigates the reproducibility of computational science research and identifies key challenges facing the community today. It is the result of the First Summer School on Experimental Methodology in Computational Science…
Sequential sentence classification deals with the categorisation of sentences based on their content and context. Applied to scientific texts, it enables the automatic structuring of research papers and the improvement of academic search…
Statistics educators recommend teaching with real data with relevant contexts, but defining relevancy is challenging and varies by student. We investigated whether providing student choice of data context increases engagement through a…
The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically…
We develop a set of methods to improve on the results of self-supervised learning using context. We start with a baseline of patch based arrangement context learning and go from there. Our methods address some overt problems such as…