Distributed Framework for Adaptive Explanatory Visualization

AbstractEducational tools designed to help students understand programming paradigms and learn programming languages are an important component of many academic curricula. This paper presents the architecture of a distributed event-based visualization system. We describe specialized content provision and visualization services and present two communication protocols in an attempt to explore the possibility of a standardized language

A Simple, Language-Independent Approach to Identifying Potentially At-Risk Introductory Programming Students

The Twenty-Third Australasian Computing Education Conference (ACE '21), Virtual Event, 2-4 February 2021For decades computing educators have been trying to identify and predict at-risk students, particularly early in the first programming course. These efforts range from the analyzing demographic data that pre-exists undergraduate entrance to using instruments such as concept inventories, to the analysis of data arising during education. Such efforts have had varying degrees of success, have not seen widespread adoption, and have left room for improvement. We analyse results from a two-year study with several hundred students in the first year of programming, comprising majors and non-majors. We find evidence supporting a hypothesis that engagement with extra credit assessment provides an effective method of differentiating students who are not at risk from those who may be. Further, this method can be used to predict risk early in the semester, as any engagement-not necessarily completion-is enough to make this differentiation. Additionally, we show that this approach is not dependent on any one programming language. In fact, the extra credit opportunities need not even involve programming. Our results may be of interest to educators, as well as researchers who may want to replicate these results in other settings.National Science Foundatio

Beach Morphological Characteristics and Coastal Processes Along Dakshina Kannada Coast, West Coast of India

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Professional Competencies in Computing Education

Competency-based learning has been a successful pedagogical approach for centuries, but only recently has it gained traction within computing. Competencies, as defined in Computing Curricula 2020, comprise knowledge, skills, and professional dispositions. Building on recent developments in competency and computing education, this working group examined relevant pedagogical theories, investigates various skill frameworks, reviewed competencies and standard practices in other professional disciplines such as medicine and law. It also investigated the integrative nature of content knowledge, skills, and professional dispositions in defining professional competencies in computing education. In addition, the group explored appropriate pedagogies and competency assessment approaches. It also developed guidelines for evaluating student achievement against relevant professional competency frameworks and explores partnering with employers to offer students genuine professional experience. Finally, possible challenges and opportunities in moving from traditional knowledge-based to competency-based education were also examined. This report makes recommendations to inspire educators of future computing professionals and smooth students' transition from academia to employment

All that glitters (in the lab) may not be gold (in the field)

Abstract. AI-ED community has hewed to rigorous evaluation of software tutors and their features. Most of these evaluations were done in-ovo or in-vivo. Can the results of these evaluations be replicated in in-natura evaluations? In our experience, the evidence for such replication has been mixed. We propose that the features of tutors that are found to be effective in-ovo/in-vivo might need motivational supports to also be effective in-natura. We speculate that some features may not transfer to in-natura use even with supports. Recognition of these issues might bridge the gap between AI-ED community and educational community at large