Scoping a vision for formative e-assessment: a project report for JISC

Assessment is an integral part of teaching and learning. If the relationship between teaching and learning were causal, i. e. if students always mastered the intended learning outcomes of a particular sequence of instruction, assessment would be superfluous. Experience and research suggest this is not the case: what is learnt can often be quite different from what is taught. Formative assessment is motivated by a concern with the elicitation of relevant information about student understanding and / or achievement, its interpretation and an exploration of how it can lead to actions that result in better learning. In the context of a policy drive towards technology-enhanced approaches to teaching and learning, the question of the role of digital technologies is key and it is the latter on which this project particularly focuses. The project and its deliverables have been informed by recent and relevant literature, in particular recent work by Black andIn this work, they put forward a framework which suggests that assessment for learning their term for formative assessment can be conceptualised as consisting of a number of aspects and five keystrategies. The key aspects revolve around the where the learner is going, where the learner is right now and how she can get there and examines the role played by the teacher, peers and the learner. Language: English Keywords: assessments, case studies, design patterns, e-assessmen

Creating Bridges: The Role of Exploratory Design Research in an Intelligent Tutoring System Project

Designers of Intelligent Tutoring Systems (ITS) have long been interested in delivering personalised teaching to individual students, typically by ensuring that the student receives content appropriate to their skills and knowledge. Nonetheless, a more holistic view on what constitutes good teaching practice has challenged whether this approach to user modelling is sufficient. Teaching is not only defined by what is taught, but also by how it is taught. In this paper, we demonstrate that exploratory design research can support this view by generating a more inclusive set of user attributes for purposes of user modelling. Through a case study, we show that design research for user modelling can function as a boundary object serving three important roles, that underpin more specifically the design of user modelling and more broadly ITS design. First, design research can establish common ground by encapsulating domain knowledge in an accessible form. This can support diverse project stakeholders to make decisions on what is to be modelled. Second, design research can reveal a wide range of teaching and learning perspectives that in turn introduce transparency to the decision-making process of user modelling and provoke a sense of criticality and accountability amongst project stakeholders. Third, design research can build new bridges between the design of the technology and the user model that underpins it. To this end, user attributes deemed important, yet too complex or cumbersome to develop, can become design principles in the context of the overall ITS design

Formative E-Assessment of Schema Acquisition in the Human Lexicon as a Tool in Adaptive Online Instruction

This chapter presents a comprehensive method of implementing e-assessment in adaptive e-instruction systems. Specifically, a neural net classifier capable of discerning whether a student has integrated new schema-related concepts from course content into her/his lexicon is used by an expert system with a database containing natural mental representations from course content obtained from students and teachers for adapting e-instruction. Mental representation modeling is used to improve student modeling. Implications for adaptive hypermedia systems and hypertext-based instructions are discussed. Furthermore, it is argued that the current research constitutes a new cognitive science empirical direction to evaluate knowledge acquisition based on meaning information

A generic architecture for interactive intelligent tutoring systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 07/06/2001.This research is focused on developing a generic intelligent architecture for an interactive tutoring system. A review of the literature in the areas of instructional theories, cognitive and social views of learning, intelligent tutoring systems development methodologies, and knowledge representation methods was conducted. As a result, a generic ITS development architecture (GeNisa) has been proposed, which combines the features of knowledge base systems (KBS) with object-oriented methodology. The GeNisa architecture consists of the following components: a tutorial events communication module, which encapsulates the interactive processes and other independent computations between different components; a software design toolkit; and an autonomous knowledge acquisition from a probabilistic knowledge base. A graphical application development environment includes tools to support application development, and learning environments and which use a case scenario as a basis for instruction. The generic architecture is designed to support client-side execution in a Web browser environment, and further testing will show that it can disseminate applications over the World Wide Web. Such an architecture can be adapted to different teaching styles and domains, and reusing instructional materials automatically can reduce the effort of the courseware developer (hence cost and time) in authoring new materials. GeNisa was implemented using Java scripts, and subsequently evaluated at various commercial and academic organisations. Parameters chosen for the evaluation include quality of courseware, relevancy of case scenarios, portability to other platforms, ease of use, content, user-friendliness, screen display, clarity, topic interest, and overall satisfaction with GeNisa. In general, the evaluation focused on the novel characteristics and performances of the GeNisa architecture in comparison with other ITS and the results obtained are discussed and analysed. On the basis of the experience gained during the literature research and GeNisa development and evaluation. a generic methodology for ITS development is proposed as well as the requirements for the further development of ITS tools. Finally, conclusions are drawn and areas for further research are identified

Setting an Agenda for Urban AI Adaptivity in Urban Planning and Architecture E-learning

The rapid spread of technology and learning systems have altered the viewpoint about the lack of E-learning to the human element. The intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education. However, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields. Building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development Massive Open Online Courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making. Therefore, the aim is to explore how educators from the architecture and urban field E-Learning stakeholders perceive AI in the creation of urban Moocs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI. In an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning?To achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges. This overview informed and guided the formulation of the survey questions. Then we conducted a survey on educators in Architecture and urban fields from universities in Egypt. The unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators\u27 AI adaptivity in online learning and MOOCs. The study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners

Setting an Agenda for Urban AI Adaptivity in Urban Planning and Architecture E-learning

The rapid spread of technology and learning systems have altered the viewpoint about the lack of E-learning to the human element. The intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education. However, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields. Building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development Massive Open Online Courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making. Therefore, the aim is to explore how educators from the architecture and urban field E-Learning stakeholders perceive AI in the creation of urban Moocs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI. In an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning?To achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges. This overview informed and guided the formulation of the survey questions. Then we conducted a survey on educators in Architecture and urban fields from universities in Egypt. The unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators\u27 AI adaptivity in online learning and MOOCs. The study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners

Developing Student Model for Intelligent Tutoring System

The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching community to understand the learning style of their students and to cater for the needs of their students. One such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful, constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in planning the training path, supplying feedback information to the pedagogical module of the system. Added to it, the student model is the preliminary component, which stores the information to the specific individual learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural network. Further, neural network and psychometric analysis were used for understanding the student characteristic and determining the student’s classification with respect to their ability. Thus, this study focused on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS by applying the neural network and psychometric analysis. The findings of this research showed that even though the linear regression between real test scores and that of the Final exam scores were marginally weak (37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model a good fit for clustering students in groups according to their common characteristics. This finding is in line with that of the findings discussed in the literature review of this study. Further, the outcome of this research is most likely to generate a new dimension for cluster based student modelling approaches for an online learning environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and neural network for student classification makes this study unique towards the development of a new student model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS system for an online learning environment. (Abstract by Author

Multimodal Emotion Recognition for Assessment of Learning in a Game-Based Communication Skills Training

This paper describes how our FILTWAM software artifacts for face and voice emotion recognition will be used for assessing learners' progress and providing adequate feedback in an online game-based communication skills training. This constitutes an example of in-game assessment for mainly formative purposes. During this training, learners are requested to mimic specific emotions via a webcam and a microphone in which the software artifacts determine the adequacy of the mimicked emotion from either face and/or voice. Our previous studies have shown that these software artifacts are able to detect face and voice emotions in real-time and with sufficient reliability. In our current work, we present a software system architecture that unobtrusively monitors learners’ behaviors in an online game- based approach and offers timely and relevant feedback based upon learner’s face and voice expressions. Whereas emotion detection is often used for adapting learning content or learning tasks, our approach focuses on using emotions for guiding learners towards improved communication skills. Herein, learners need to have an opportunity of frequent guided practice in order to learn how to express the right emotion at the right time. We assume that this approach can address several issues with the current trainings in this area. We sketch the research design of our planned study that investigates the efficiency, effectiveness and enjoyableness of our approach. We conclude the paper by considering the challenges of this study.The Netherlands Laboratory for Lifelong Learning (NELLL) of the Open University of the Netherlands

El uso del chatbot como elemento de acción tutorial en la enseñanza universitaria

Abstract: It is of great importance to help and pay attention to students through different educational activities to ensure their participation in class and thus reduce the dropout rate. Traditionally, tutoring activities have been limited to face-to-face sessions in which students pose questions to the teacher. However, in a connected world with many available information systems, innovative tools are needed to facilitate and speed up both the study and the resolution of doubts in a comfortable way. Methods: This paper proposes using a chatbot based tutoring system as a novel educational experience focused on motivating universities students. Results: Besides, we provide a proof-of-concept implementation of a chatbot that answers questions as quickly and accurately possible at any time, in a comfortable way for the students, and at the same time it gathers feedback from the students regarding those topics that need to be explained in class in more detail. Conclusions: This experience is intended to increase the engagement and collaboration of both students and instructors and has helped to decrease the dropout rate in recent years.Resumen: Es de vital importancia ayudar y guiar el aprendizaje de los estudiantes a través de diferentes herramientas y actividades educativas que faciliten su participación en clase y permitan reducir la tasa de abandono. Tradicionalmente, las actividades de tutorización para estudiantes universitarios, se limita a reuniones presenciales en las que los estudiantes plantean preguntas al docente. Sin embargo, dadas las circunstancias actuales y ante un mundo conectado con muchos sistemas de información disponibles, se necesitan herramientas docentes innovadoras que faciliten el aprendizaje y una ágil resolución de dudas. Método: en este trabajo se propone la utilización de un sistema de tutorías, basado en el uso de un chatbot como experiencia educativa novedosa y orientada a motivar y facilitar el aprendizaje en estudiantes universitarios. Resultados: estudio aporta la implementación de un chatbot que responde de forma rápida y precisa, disponible en cualquier momento para solucionar dudas y facilitar el estudio de las materias a los estudiantes. Este chatbot además permite recopilar comentarios de los propios estudiantes sobre los temas que requieren ser explicados en clase con un mayor detalle. Conclusiones: El uso del chatbot tutorial, ha permitido aumentar el compromiso y la colaboración tanto de los estudiantes como de los docentes, disminuyendo la tasa del número de estudiantes que abandonan la asignatura.Ministerio Español de Economía y Competitividad - project TIN2017-85727-C4-2-P -UGR-DeepBio