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

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

Using Enhanced Concept Map for Student Modeling in Programming

We have been using the concept map of the domain, enhanced with pedagogical concepts called learning objectives, as the overlay student model in our intelligent tutors for programming. The resulting student model is finegrained, and has several advantages: (1) it facilitates better adaptation during problem generation; (2) it makes it possible for the tutor to automatically vary the level of locality during problem generation to meet the needs of the learner; (3) it clarifies to the learner the relationship among domain concepts when opened to scrutiny; (4) the tutor can estimate the level of understanding of a student in any higher-level concept, not just the concepts for which it presents problems; and (5) two tutors in a domain can affect each other’s adaptation of problems. Prior evaluations have shown that tutors that use enhanced concept maps help improve learning. Student Modeling Traditionally, student models in tutoring systems have consisted of cognitive, affective and inferential components. The cognitive student model has been popularly built as an overlay of the domain model. Researchers have used various organizations for the domain model and the resulting overlay cognitive student model. These representations include conceptual graphs [5], Bayesian networks [22], directed acyclic graphs [10], tables [3] and Prolog clauses [19]. Conceptual graphs have been used because they are graphically inspectable, and facilitate interaction planning and student diagnosis [6]. Bayesian networks have been used to model cause and effect relationships among concepts (e.g., if you know 'for ' and 'while ' statements, you know loops). Tables and Prolog clauses provide a mechanism to aggregate concepts but do not explicitly represent any inherent relationships among them. Other organizations used for domain model include networks and trees. In WADEIn [2] used to teach student