Design issues for a scenario-based learning environment

This document outlines my thinking on the design of a scenario-based learning environment. The material presented falls into two distinct categories. These are presenting work completed so far and presenting current thinking on the design. As such, some of the material is based on experience over a number of years of using the approaches described and some is very tentative and needs further development. The nature of a learning environment raises issues in many domains. These include educational issues and technical implementation issues. Although I endeavour to highlight the domain of issues, some of the boundaries are not always clear. Structure of the environment is impacted by the pedagogical techniques assumed or used

Cluster analysis for tailored tutoring system

Nei corsi online, la presenza di tutor (disciplinari, tecnici, metodologici) \ue8 rilevante per la formazione degli studenti; le attivit\ue0 di tutorato influenzano la qualit\ue0 delle azioni didattiche e contribuiscono a creare ambienti di apprendimento online dinamici e attivi. La ricerca qui presentata mira a definire un modello per la creazione di sistemi di tutoraggio data-driven, strutturato e personalizzato, che rilevi gruppi omogenei di studenti in un corso di laurea erogato in modalit\ue0 prevalentemente a distanza per i quali attuare specifici interventi di supporto. Abbiamo scelto una tecnica di analisi multivariata dei dati, la cluster analysis, e raccolto, selezionato e utilizzato i dati personali e i dati relativi ai risultati accademici degli studenti del primo anno del Corso di Laurea in Digital Education dell\u2019Universit\ue0 di Modena e Reggio Emilia (n=110) per creare gruppi di studenti simili, identificare le caratteristiche comuni degli studenti in ogni gruppo e definire strategie di tutoraggio personalizzate per ogni cluster. L\u2019analisi ha permesso di individuare sei cluster omogenei di studenti e di definire alcune azioni di tutoraggio personalizzate per ciascun gruppo a partire dai risultati conseguiti negli esami del primo anno. Le attivit\ue0 da progettare e proporre riguardano l\u2019approfondimento dei contenuti, la motivazione e la metacognizione, coinvolgono tutor e docenti e possono svolgersi individualmente o in piccoli gruppi.In online courses, the tutoring activities are relevant for the learners\u2019 training; they affect the course quality and imply creating dynamic and active online learning environments. The research aims to define a model based on a structured and data-driven tutoring system to identify homogeneous groups of students attending a blended degree course and, then, set tailored interventions as support. We chose a multivariate data analysis technique, cluster analysis, and used personal data and academic achievements of first-year students (n=110) in Degree Course in Digital Education at the University of Modena and Reggio Emilia to create groups of similar learners, identify common characteristics of students in each group and define personalized tutoring strategies for each cluster. The analysis allows identifying six homogeneous clusters of students and defining activities to design that concern the fields of content, motivation and metacognition, involve degree course tutors and teachers, and be carried out individually or in small groups of students

New Trends in Second Language Learning and Teaching through the lens of ICT, Networked Learning, and Artificial Intelligence

In the last few decades, Information and Communications Technology (ICT) applications have been shaping the field of Computer Assisted Language Learning (CALL). Mobile Assisted Language Learning (MALL) paved the way for ubiquitous learning. The advent of new technologies in the early 21st century also added a social dimension to ICT that allowed for Networked Learning (NL). Given that language learning is fundamentally a socio-cultural experience, networked learning capabilities have provided the potential for language learning in community settings. This has revitalized the earlier frameworks provided by CALL. NL has empowered language learners today to connect globally, to access Open Educational Resources, and to self-regulate their learning processes beyond the scope of traditional curricula. In parallel, the rising pervasiveness of Artificial Intelligence (AI) applications and their relevance to language learning has led CALL to branch out into Intelligent CALL (ICALL). The first section of this article provides a brief historical overview of CALL, examines it through the lens of ICT, networked learning, and open access. The second section focuses on the implications of AI for creating new trends in second language education, the challenge for providing customization at scale, and raises important issues related to transparency and privacy for future research

The Development and Validation of a System for the Knowledge-Based Tutoring of Special Education Rules and Regulations

Research indicates that school officials fail to identify a relatively high proportion of school-aged children with behavioral or emotional handicaps. As a result, these children may not be receiving the special education services to which they are entitled. Multidisciplinary team members may be failing to identify these children because they lack understanding of special education rules and regulations. The purpose of this project was to combine the technologies of expert systems and mastery-based instruction to develop an inservice and preservice training program capable of producing mastery-level performance of the skills required to identify children with behavioral or emotional handicaps. Borg and Gall\u27s ( 983) research and development cycle provided the model for developing, testing, and revising the program. Prototype evaluations and large-scale field tests revealed that the program met its performance and user satisfaction objectives when administered under conditions of independent administration. However, a failure on the use and part of remote remote administrators to comply with prescribed program administration procedures allowed an unacceptable number of subjects to end training without completing all computer exercises. Attention to administration procedures contributed to the success of the project in meeting its performance and user satisfaction objectives in the final operational field test. The positive findings of the project have implications on two levels. First, the findings are important for the positive effect they may have on the lives of children. Decision-making errors on the part of multidisciplinary team members can be costly to children with behavioral or emotional handicaps, as well as to other children. The evidence obtained in this project suggests that multidisciplinary team members can be trained to accurately identify children with behavioral or emotional handicaps. On another, and perhaps more important, level, the findings have implications for the design of effective inservice and preservice training programs. The application of innovative technologies to inservice and preservice training problems does not necessarily result in the development of products capable of producing mastery-level decision-making performance. The positive results achieved in the present project suggest that those seeking to apply innovative technologies to inservice and preservice training problems take into account basic instructional design principles

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Proceedings of the 1993 Conference on Intelligent Computer-Aided Training and Virtual Environment Technology, Volume 1

These proceedings are organized in the same manner as the conference's contributed sessions, with the papers grouped by topic area. These areas are as follows: VE (virtual environment) training for Space Flight, Virtual Environment Hardware, Knowledge Aquisition for ICAT (Intelligent Computer-Aided Training) & VE, Multimedia in ICAT Systems, VE in Training & Education (1 & 2), Virtual Environment Software (1 & 2), Models in ICAT systems, ICAT Commercial Applications, ICAT Architectures & Authoring Systems, ICAT Education & Medical Applications, Assessing VE for Training, VE & Human Systems (1 & 2), ICAT Theory & Natural Language, ICAT Applications in the Military, VE Applications in Engineering, Knowledge Acquisition for ICAT, and ICAT Applications in Aerospace