14 research outputs found
Examples and tutored problems: How can self-explanation make a difference to learning?
âThe final publication is
available at link.springer.comâ.Learning from worked examples has been shown to be superior to unsupported problem solving in numerous studies. Examples reduce the cognitive load on the learner's working memory, thus helping the student to learn faster or deal with more complex questions. Only recently researchers started investigating the worked example effect in Intelligent Tutoring Systems (ITSs). We conducted a study to investigate the effect of using worked examples in combination with supported problem-solving in SQL-Tutor. We had three conditions: Examples Only (EO), Problems Only (PO), and Alternating Examples/Problems (AEP). After completing a problem, students received a self-explanation prompt that focused on the concepts used in the problem, to make sure that students acquire conceptual knowledge. On the other hand, examples were followed by self-explanation prompts that focused on procedural knowledge. The study showed that the AEP and PO conditions outperformed EO in learning gain, while AEP outperformed PO in conceptual knowledge acquisition. Therefore, interleaving examples with supported problems is an optimal choice compared to using examples or supported problems only in SQL-Tutor
An Interaction Centred Approach to the teaching of Non-technical Skills in a Virtual Environment
In most domains involving expert knowledge, there is a number of cognitive and social factors influencing how efficient one human being is at correctly assessing and responding to certain situations. These factors, which contribute to the efficient and safe realization of a technical activity, are known as non-technical skills, and correspond to a wide range of cognitive proficiencies such as situation awareness, decision making, stress or fatigue management, but also social skills such as communication, leadership and team working. Different studies have shown the impact such skills can have in the successful resolving of a number of critical situations, even more so in our domains of interest which are medical surgery or driving. In this paper, we take a look at the difficulties raised by the teaching of the technical and non-technical skills mobilized during a critical situation, in the context of TEL within virtual environments. We present the advantages of using a combined enactive and situated learning approach to this problematic, and then take an ill-defined perspective to raise some important designing issues in this respect. We show that some aspects of this problem have not been encompassed yet in the ill-defined domains literature, and should be further studied in any attempt at teaching behaviours inducing technical and non-technical skills in a virtual world
Supporting students in the analysis of case studies for professional ethics education
Intelligent tutoring systems and computer-supported collaborative environments have been designed to enhance human learning in various domains. While a number of solid techniques have been developed in the Artificial Intelligence in Education (AIED) field to foster human learning in fundamental science domains, there is still a lack of evidence about how to support learning in so-called ill-defined domains that are characterized by the absence of formal domain theories, uncertainty about best solution strategies and teaching practices, and learners' answers represented through text and argumentation.
This dissertation investigates how to support students' learning in the ill-defined domain of professional ethics through a computer-based learning system. More specifically, it examines how to support students in the analysis of case studies, which is a common pedagogical practice in the ethics domain.
This dissertation describes our design considerations and a resulting system called Umka. In Umka learners analyze case studies individually and collaboratively that pose some ethical or professional dilemmas. Umka provides various types of support to learners in the analysis task. In the individual analysis it provides various kinds of feedback to arguments of learners based on predefined system knowledge. In the collaborative analysis Umka fosters learners' interactions and self-reflection through system suggestions and a specifically designed visualization. The system suggestions offer learners the chance to consider certain helpful arguments of their peers, or to interact with certain helpful peers. The visualization highlights similarities and differences between the learners' positions, and illustrates the learners' level of acceptance of each other's positions.
This dissertation reports on a series of experiments in which we evaluated the effectiveness of Umka's support features, and suggests several research contributions.
Through this work, it is shown that despite the ill-definedness of the ethics domain, and the consequent complications of text processing and domain modelling, it is possible to build effective tutoring systems for supporting students' learning in this domain. Moreover, the techniques developed through this research for the ethics domain can be readily expanded to other ill-defined domains, where argument, qualitative analysis, metacognition and interaction over case studies are key pedagogical practices
Intelligent Mentoring Systems for Making Meaning from Work Experience
This position paper presents a forward-looking view on addressing a long standing professional learning challenge faced by higher educational institutions, namely assisting students to make meaning from work-based experience and develop as reflexive professionals. We suggest that a synergetic approach, building on existing research in professional lifelong learning and intelligent learning environments and taking advantage of new opportunities provided by emerging technologies, will underpin a new breed of intelligent mentoring systems for professional learning. They will foster the learnersâ meaning making process, as well as assist tutors in their roles as coaches/mentors
Reflective Experiential Learning: Using Active Video Watching for Soft Skills Training
Learning by watching videos has become the dominant way of learning for millennials. However, watching videos is a passive form of learning which usually results in a low level of engagement. As the result, video-based learning often results in poor learning outcomes. One of the proven strategies to increase engagement is to integrate interactive activities such as quizzes and assessment problems into videos. Although this strategy increases engagement, it requires changing existing videos and therefore substantial effort from the teacher. We have developed the Active Video Watching (AVW) system that enables the teacher to use existing videos from YouTube without modifications. The teacher is required to define a set of aspects for videos, which serve as reflective scaffolds in order to increase engagement and focus learnersâ thinking. AVW provides a Personal Space for individual learners to link their personal experiences while watching videos. The comments collected can be used by the individuals to reflect on their own thoughts or to be shared with other learners in the Social Space. We conducted a study with postgraduate students on presentation skills. The results show that the level of engagement with AVW was high, and that the aspects were effective as reflection prompts. We plan to conduct further studies related to other types of soft skills, and also to further extend AVW to provide individualized feedback to students
Clustering student interaction data using Bloom's Taxonomy to find predictive reading patterns
In modern educational technology we have the ability to capture click-stream interaction data from a student as they work on educational problems within an online environment. This provides us with an opportunity to identify student behaviours within the data (captured by the online environment) that are predictive of student success or failure. The constraints that exist within an educational setting provide the ability to associate these student behaviours to specific educational outcomes. This information could be then used to inform environments that support student learning while improving a studentâs metacognitive skills.
In this dissertation, we describe how reading behaviour clusters were extracted in an experiment in which students were embedded in a learning environment where they read documents and answered questions. We tracked their keystroke level behaviour and then applied clustering techniques to find pedagogically meaningful clusters. The key to finding these clusters were categorizing the questions as to their level in Bloomâs educational taxonomy: different behaviour patterns predicted success and failure in answering questions at various levels of Bloom. The clusters found in the first experiment were confirmed through two further experiments that explored variations in the number, type, and length of documents and the kinds of questions asked. In the final experiment, we also went beyond the actual keystrokes and explored how the pauses between keystrokes as a student answers a question can be utilized in the process of determining student success.
This research suggests that it should be possible to diagnose learner behaviour even in âill-definedâ domains like reading. It also suggests that Bloomâs taxonomy can be an important (even necessary) input to such diagnosis
Un modÚle pour la génération d'indices par une plateforme de tuteurs par traçage de modÚle
La prĂ©sente thĂšse dĂ©crit des travaux de recherche effectuĂ©s dans le domaine des systĂšmes tutoriels intelligents (STI). Plus particuliĂšrement, elle s'intĂ©resse aux tuteurs par traçage de modĂšle (MTT). Les MTTs ont montrĂ© leur efficacitĂ© pour le tutorat de la rĂ©solution de tĂąches bien dĂ©finies. Par contre, les interventions pĂ©dagogiques qu'ils produisent doivent ĂȘtre incluses, par l'auteur du tuteur, dans le modĂšle de la tĂąche enseignĂ©e. La recherche effectuĂ©e rĂ©pond Ă cette limite en proposant des mĂ©thodes et algorithmes permettant la gĂ©nĂ©ration automatique d'interventions pĂ©dagogiques. Une mĂ©thode a Ă©tĂ© dĂ©veloppĂ©e afin de permettre Ă la plateforme Astus de gĂ©nĂ©rer des indices par rapport Ă la prochaine Ă©tape en examinant le contenu du modĂšle de la tĂąche enseignĂ©e. De plus, un algorithme a Ă©tĂ© conçu afin de diagnostiquer les erreurs des apprenants en fonction des actions hors trace qu'ils commettent. Ce diagnostic permet Ă Astus d'offrir une rĂ©troaction par rapport aux erreurs sans que l'auteur du tuteur ait Ă explicitement modĂ©liser les erreurs. Cinq expĂ©rimentations ont Ă©tĂ© effectuĂ©es lors de cours enseignĂ©s au dĂ©partement d'informatique de l'UniversitĂ© de Sherbrooke afin de valider de façon empirique les interventions gĂ©nĂ©rĂ©es par Astus. Le rĂ©sultat de ces expĂ©rimentations montre que 1) il est possible de gĂ©nĂ©rer des indices par rapport Ă la prochaine Ă©tape qui sont aussi efficaces et aussi apprĂ©ciĂ©s que ceux conçus par un enseignant et que 2) la plateforme Astus est en mesure de diagnostiquer un grand nombre d'actions hors trace des apprenants afin de fournir une rĂ©troaction par rapport aux erreurs
Augmented Conversation and Cognitive Apprenticeship Metamodel Based Intelligent Learning Activity Builder System
This research focused on a formal (theory based) approach to designing Intelligent Tutoring System (ITS) authoring tool involving two specific conventional pedagogical theoriesâConversation Theory (CT) and Cognitive Apprenticeship (CA). The research conceptualised an Augmented Conversation and Cognitive Apprenticeship Metamodel (ACCAM) based on apriori theoretical knowledge and assumptions of its underlying theories. ACCAM was implemented in an Intelligent Learning Activity Builder System (ILABS)âan ITS authoring tool. ACCAMâs implementation aims to facilitate formally designed tutoring systems, hence, ILABSâthe practical implementation of ACCAMâ constructs metamodels for Intelligent Learning Activity Tools (ILATs) in a numerical problem-solving context (focusing on the construction of procedural knowledge in applied numerical disciplines). Also, an Intelligent Learning Activity Management System (ILAMS), although not the focus of this research, was developed as a launchpad for ILATs constructed and to administer learning activities. Hence, ACCAM and ILABS constitute the conceptual and practical contributions that respectively flow from this research.
ACCAMâs implementation was tested through the evaluation of ILABS and ILATs within an applied numerical domainâthe accounting domain. The evaluation focused on the key constructs of ACCAMâcognitive visibility and conversation, implemented through a tutoring strategy employing Process Monitoring (PM). PM augments conversation within a cognitive apprenticeship framework; it aims to improve the visibility of the cognitive process of a learner and infers intelligence in tutoring systems. PM was implemented via an interface that attempts to bring learnerâs thought process to the surface. This approach contrasted with previous studies that adopted standard Artificial Intelligence (AI) based inference techniques. The interface-based PM extends the existing CT and CA work. The strategy (i.e. interface-based PM) makes available a new tutoring approach that aimed fine-grain (or step-wise) feedbacks, unlike the goal-oriented feedbacks of model-tracing. The impact of PMâas a preventive strategy (or intervention) and to aid diagnosis of learnersâ cognitive processâwas investigated in relation to other constructs from the literature (such as detection of misconception, feedback generation and perceived learning effectiveness). Thus, the conceptualisation and implementation of PM via an interface also contributes to knowledge and practice.
The evaluation of the ACCAM-based design approach and investigation of the above mentioned constructs were undertaken through usersâ reaction/perception to ILABS and ILAT. This involved, principally, quantitative approach. However, a qualitative approach was also utilised to gain deeper insight. Findings from the evaluation supports the formal (theory based) design approachâthe design of ILABS through interaction with ACCAM. Empirical data revealed the presence of conversation and cognitive visibility constructs in ILATs, which were determined through its behaviour during the learning process. This research identified some other theoretical elements (e.g. motivation, reflection, remediation, evaluation, etc.) that possibly play out in a learning process. This clarifies key conceptual variables that should be considered when constructing tutoring systems for applied numerical disciplines (e.g. accounting, engineering). Also, the research revealed that PM enhances the detection of a learnerâs misconception and feedback generation. Nevertheless, qualitative data revealed that frequent feedbacks due to the implementation of PM could be obstructive to thought process at advance stage of learning. Thus, PM implementations should also include delayed diagnosis, especially for advance learners who prefer to have it on request. Despite that, current implementation allows users to turn PM off, thereby using alternative learning route. Overall, the research revealed that the implementation of interface-based PM (i.e. conversation and cognitive visibility) improved the visibility of learnerâs cognitive process, and this in turn enhanced learningâas perceived
Revisiting Ill-Definedness and the Consequences for ITSs
ITSs for ill-defined domains have attracted a lot of attention recently, which is well-deserved, as such ITSs are hard to develop. The first step towards such ITSs is reaching a wide agreement about the terminology used in the area. In this paper, we discuss the two important dimensions of ill-definedness: the domain and the instructional task. By the domain we assume declarative domain knowledge, or the domain theory, while the instructional task is the task the student is learning, in terms of problem-solving skills. It is possible to have a well-defined domain and still have ill-defined instructional tasks in the same domain. We look deeper at the features of ill-defined tasks, which all contribute to their ill/well defined nature. The paper discusses model-tracing and constraint-based modeling, in terms of their suitability for ill-defined tasks and domains. We show that constraint-based modeling can be used in both well- and ill-defined domains, and illustrate our conclusion using several instructional tasks
Astus, une plateforme pour créer et étudier les systÚmes tutoriels intelligents « par traçage de modÚle »
Cette thĂšse sâintĂ©resse aux systĂšmes tutoriels intelligents (STI), un type dâenvironnement informatique pour lâapprentissage humain (EIAH) qui se distingue des autres (p. ex. les exerciseurs et les hypermĂ©dias Ă©ducatifs) en offrant un mĂ©canisme dâĂ©valuation plus sophistiquĂ©. Parmi les diffĂ©rentes familles de STI, ce sont les STI « par traçage de modĂšle » (MTT) qui ont le plus fait leurs preuves.
Les MTT sont critiquĂ©s, premiĂšrement parce quâils Ă©valuent lâapprenant de façon serrĂ©e (c.-Ă -d. qui positionne lâaction de lâapprenant par rapport Ă une ou plusieurs mĂ©thodes pour effectuer la tĂąche), ce qui nâest possible que pour des tĂąches bien dĂ©finies. Par consĂ©quent, on leur reproche dâencourager un apprentissage superficiel. DeuxiĂšmement, parce que les efforts de crĂ©ation quâils requiĂšrent sont jugĂ©s prohibitifs, ce qui a menĂ© Ă lâapparition dâautres familles de STI, comme les STI « par contraintes » et les STI « par traçage dâexemples » et ceux basĂ©s sur lâapprentissage automatique.
Par cette thĂšse, nous voulons contribuer Ă renouveler lâintĂ©rĂȘt pour les MTT en amĂ©liorant le rapport entre les efforts de crĂ©ation et lâefficacitĂ© potentielle des interventions, et en Ă©tablissant plus clairement leur rĂŽle pĂ©dagogique.
Pour ce faire, nous proposons la plateforme Astus qui permet dâexplorer lâespace qui existe entre les MTT crĂ©Ă©s avec les plateformes existantes, et des MTT dĂ©diĂ©s ayant recours Ă des connaissances didactiques sophistiquĂ©es (p. ex. des dialogues) qui exigent des efforts de crĂ©ation encore plus importants.
La plateforme Astus se distingue des plateformes existantes parce quâelle gĂ©nĂšre des interventions plutĂŽt que de recourir Ă des interventions prĂ©mĂąchĂ©es et quâelle supporte les tĂąches sâeffectuant dans des environnements qui ont une dimension physique. La gĂ©nĂ©ration des interventions dĂ©pend :
dâun modĂšle de la tĂąche qui sâinscrit dans le paradigme du tuteur, câest-Ă -dire qui reprĂ©sente une abstraction et une gĂ©nĂ©ralisation des instructions dâun tuteur humain;
dâun modĂšle de lâUI qui permet des interventions riches comme une dĂ©monstration (c.-Ă -d. dĂ©placements du pointeur et simulation des clics et des saisies);
de langages dĂ©diĂ©s et dâoutils qui rĂ©duisent les efforts de crĂ©ation des auteurs;
de mĂ©canismes dâextension qui permettent dâadapter la gĂ©nĂ©ration en fonction dâune stratĂ©gie pĂ©dagogique particuliĂšre.
Le paradigme du tuteur, parce quâil favorise une communication transparente entre le systĂšme et lâapprenant, met en Ă©vidence les avantages et les dĂ©savantages de lâapproche pĂ©dagogique des MTT, essentiellement une Ă©valuation prĂ©cise (c.-Ă -d. qui permet de produire des indices sur la prochaine Ă©tape et des rĂ©troactions sur les erreurs), mais serrĂ©e.
En sâinscrivant explicitement le paradigme du tuteur, entre autres en Ă©vitant de tirer profit de la nature de domaines particuliers ou de propriĂ©tĂ©s de tĂąches particuliĂšres pour assouplir lâĂ©valuation, la plateforme Astus se dĂ©marque plus nettement des autres familles de STI que les autres MTT. Par consĂ©quent, elle Ă©tablit plus clairement le rĂŽle pĂ©dagogique des MTT.
Cinq expĂ©rimentations (menĂ©es par Luc Paquette) Ă petite Ă©chelle ont Ă©tĂ© rĂ©alisĂ©es auprĂšs dâĂ©tudiants au baccalaurĂ©at au dĂ©partement dâinformatique (un laboratoire pour la manipulation dâarbres binaires de recherche et un pour la conversion de nombres en virgule flottante). Ces expĂ©rimentations indiquent que les interventions gĂ©nĂ©rĂ©es sont efficaces. Au-delĂ de ces rĂ©sultats, câest le processus entourant ces expĂ©rimentations, parce quâil est comparable au processus des chercheurs potentiellement intĂ©ressĂ©s par la plateforme Astus, qui montre que la version prĂ©sentĂ©e dans cette thĂšse est plus quâun prototype et quâelle peut ĂȘtre utilisĂ©e Ă lâinterne dans un contexte rĂ©el