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

A Literature Review on Intelligent Services Applied to Distance Learning

Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio

Analisi di tassi di completamento e abbandono nei MOOC di EduOpen

The completion rate of massive open online courses (MOOCs) is generally less than 10% of participants. This is due to several factors, many of which cannot be eliminated: spontaneous enrolment, participants’ extreme heterogeneity, self-regulated processes and differences in motivational and cultural profiles. One of the factors that can affect the rate of completing a MOOC is the modality of delivery. The active presence of theteacher and of other support figures in MOOCs, even where criticality is linked to the number of students and the management of the dynamics present in the online learning environment, can qualitatively and quantitatively affect both the levels of interaction and participation of the users and the completion percentages of the course itself. The MOOCs published on the EduOpen Portal provide two specific methods of use: selfpaced and tutoring. The choice of modality, which is defined in the design phase, “impacts” the structure and timing of the course itself, its learning objectives and the types of teaching resources. Consequently, the levels of interaction and evaluation processes are also “calibrated” in relation to the “presence or absence” of support figures in the online environment. The contribution, starting from the first data generated by the Learning Analytics system of the Portal, focuses on analysis of the percentage of the completion/ dropout rate recorded for the entire group of MOOCs published in relation to the delivery methods defined in the design phase of the various courses. In July 2019 there were 247 courses in the catalogue with more than 55,000 users. The final objective of the analysis is to include in the guidelines for the design of a MOOC the results of this first study.Il tasso di completamento di MOOCs e generalmente inferiore al 10% degli iscritti. Questo a causa di diversi fattori, molti non eliminabili, quali: reclutamento spontaneo, estrema eterogeneità degli iscritti, processi di autoregolazione, differenze nei profili motivazionali e culturali. Uno dei fattori che può incidere sul tasso di completamento di un MOOC e rappresentato dalla modalità di erogazione. La presenza attiva del docente e di altre figure di supporto in corsi MOOCs, se pur con le evidenti criticità legate alla numerosità degli studenti e alla gestione delle dinamiche presenti dall’ambiente di apprendimento online può incidere (qualitativamente e quantitativamente) sia sui livelli di interazione e partecipazione degli utenti sia sulle percentuali di completamento del corso stesso. I MOOCs pubblicati sul Portale EduOpen prevedono nello specifico due modalità di fruizione: autoapprendimento e tutorata. La scelta della modalità - definita in fase progettuale - “impatta” sulla struttura e sulle tempistiche stesse del corso, sugli obiettivi di apprendimento e sulla tipologia delle risorse didattiche. Di conseguenza, i livelli di interazione e i processi di valutazione sono “calibrati” anche in relazione “alla presenza o all’assenza” di figure di supporto nell’ambiente online. Il contributo, a partire dai primi dati generati dal sistema di Learning Analytics del portale, si focalizza sull’analisi delle percentuali di completamento/tasso di abbandono registrate sull’intero insieme di MOOCs pubblicati in relazione alle modalità di erogazione definite nella fase di progettazione dei vari corsi. A luglio 2019 i corsi presenti nel catalogo sono 247 con un numero di utenti superiore a 55000 utenti. L’obiettivo finale dell’analisi e quello di includere nelle linee guida alla progettazione dei MOOCs i risultati emersi da questa prima ricerca

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

Player agency in interactive narrative: audience, actor & author

The question motivating this review paper is, how can computer-based interactive narrative be used as a constructivist learn- ing activity? The paper proposes that player agency can be used to link interactive narrative to learner agency in constructivist theory, and to classify approaches to interactive narrative. The traditional question driving research in interactive narrative is, ‘how can an in- teractive narrative deal with a high degree of player agency, while maintaining a coherent and well-formed narrative?’ This question derives from an Aristotelian approach to interactive narrative that, as the question shows, is inherently antagonistic to player agency. Within this approach, player agency must be restricted and manip- ulated to maintain the narrative. Two alternative approaches based on Brecht’s Epic Theatre and Boal’s Theatre of the Oppressed are reviewed. If a Boalian approach to interactive narrative is taken the conflict between narrative and player agency dissolves. The question that emerges from this approach is quite different from the traditional question above, and presents a more useful approach to applying in- teractive narrative as a constructivist learning activity

Information Technology decision makers’ readiness for artificial intelligence governance in institutions of higher education in South Africa

Artificial intelligence (AI) can enhance the educational experience for academics and students. However, research has inadequately examined AI ethics and governance, particularly in the higher education sector of developing economies such as South Africa. AI governance ensures that envisioned AI benefits are realized while reducing AI risks. Against this backdrop of huge research deficit, the current study reports on a qualitative exploratory study that investigates the state of readiness for AI governance and AI governance maturity in South African higher education institutions. Informed by the combination of the TOE framework, the traditional IT governance model and the adapted IT governance maturity assessment model, semi-structured interviews were conducted with academic and ICT decision makers from two public and three private higher education institutions in South Africa to determine their insights on the state of readiness and maturity of AI governance. Results reveal high proliferation of AI elements in higher education information systems. However, results revealed low levels of AI governance readiness by higher education institutions. The study recommends for recognition of AI risks and taking lessons from AI regulatory frameworks advanced in developed countries

Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (its)

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System\u27s (ITS) coaching strategy based on the student\u27s mood. As a step toward this goal, this study evaluated the relationships between each student\u27s mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student\u27s performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student\u27s affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student\u27s interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student\u27s mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle\u27s (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank\u27s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables

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