Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

In the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers' workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd's research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd

Knowledge Elicitation Methods for Affect Modelling in Education

Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners’ affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners’ affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy

Towards Designing AI-Enabled Adaptive Learning Systems

Paper I, III, IV and V are not available as a part of the dissertation due to the copyright.Among the many innovations driven by artificial intelligence (AI) are more advanced learning systems known as AI-enabled adaptive learning systems (AI-ALS). AI-ALS are platforms that adapt to the learning strategies of students by modifying the order and difficulty level of learning tasks based on the abilities of students. These systems support adaptive learning, which is the personalization of learning for students in a learning system, such that the system can deal with individual differences in aptitude. AI-ALS are gaining traction due to their ability to deliver learning content and adapt to individual student needs. While the potential and importance of such systems have been well documented, the actual implementation of AI-ALS and other AI-based learning systems in real-world teaching and learning settings has not reached the effectiveness envisaged on the level of theory. Moreover, AI-ALS lack transferable insights and codification of knowledge on their design and development. The reason for this is that many previous studies were experimental. Thus, this dissertation aims to narrow the gap between experimental research and field practice by providing practical design statements that can be implemented in effective AI-ALSs.publishedVersio

E-Learning

Technology development, mainly for telecommunications and computer systems, was a key factor for the interactivity and, thus, for the expansion of e-learning. This book is divided into two parts, presenting some proposals to deal with e-learning challenges, opening up a way of learning about and discussing new methodologies to increase the interaction level of classes and implementing technical tools for helping students to make better use of e-learning resources. In the first part, the reader may find chapters mentioning the required infrastructure for e-learning models and processes, organizational practices, suggestions, implementation of methods for assessing results, and case studies focused on pedagogical aspects that can be applied generically in different environments. The second part is related to tools that can be adopted by users such as graphical tools for engineering, mobile phone networks, and techniques to build robots, among others. Moreover, part two includes some chapters dedicated specifically to e-learning areas like engineering and architecture

8. Issues in Intelligent Computer-Assisted Instruction: Eval uation and Measurement

In this chapter we plan to explore two issues in the field of intelligent computer assisted instruction (ICAI) that we feel offer opportunities to advance the state of the art. These issues are evaluation of ICAI systems and the use of the underlying technology in ICAI systems to develop tests. For each issue we will provide a theoretical context, discuss key constructs, provide a brief window to the appropriate literature, suggest methodological solutions and conclude with a concrete example of the feasibility of the solution from our own research. INTELLIGENT COMPUTER-ASSISTED INSTRUCTION (ICAI) ICAI is the application of artificial intelligence to computer-assisted instruction. Artificial intelligence, a branch of computer science, is making computers smart in order to (a) make them more useful and (b) understand intelligence (Winston, 1977). Topic areas in artificial intelligence have included natural language processing (Schank, 1980), vision (Winston, 1975), knowledge representation (Woods, 1983), spoken language (Lea, 1980), planning (Hayes-Roth, 1980), and expert systems (Buchanan, 1981). The field of Artificial Intelligence (AI) has matured in both hardware and software. The most commonly used language in the field is LISP (List Processing). A major development in the hardware area is that personal LISP machines are now available at a relatively low cost (20-50K) with the power of prior mainframes. In the software area two advances stand out: (a) programming support environments such as LOOPS (Bobrow & Stefik, 1983) and (b) expert system tools. These latter tools are now running on powerful micros. The application of expert systems technology to a host of real-world problems has demonstrated the utility of artificial intelligence techniques in a very dramatic style. Expert system technology is the branch of artificial intelligence at this point most relevant to ICAI. Expert Systems Knowledge-based systems or expert systems are a collection of problem-solving computer programs containing both factual and experiential knowledge and data in a particular domain. When the knowledge embodied in the program is a result of a human expert elicitation, these systems are called expert systems. A typical expert system consists of a knowledge base, a reasoning mechanism popularly called an inference engine and a friendly user interface. The knowledge base consists of facts, concepts, and numerical data (declarative knowledge), procedures based on experience or rules of thumb (heuristics), and causal or conditional relationships (procedural knowledge). The inference engine searches or reasons with or about the knowledge base to arrive at intermediate conclusions or final results during the course of problem solving. It effectively decides when and what knowledge should be applied, applies the knowledge and determines when an acceptable solution has been found. The inference engine employs several problem-solving strategies in arriving at conclusions. Two of the popular schemes involve starting with a good description or desired solution and working backwards to the known facts or current situation (backward chaining), and starting with the current situation or known facts and working toward a goal or desired solution (forward chaining). The user interface may give the user choices (typically menu-driven) or allow the user to participate in the control of the process (mixed initiative). The interface allows the user: to describe a problem, input knowledge or data, browse through the knowledge base, pose question, review the reasoning process of the system, intervene as necessary, and control overall system operation. Successful expert systems have been developed in fields as diverse as mineral exploration (Duda & Gaschnig, 1981) and medical diagnosis (Clancy, 1981). ICAI Systems ICAI systems use approaches artificial intelligence and cognitive science to teach a range of subject matters. Representative types of subjects include: (a) collection of facts, for example, South American geography in SCHOLAR (Carbonell & Collins, 1973); (b) complete system models, for example, a ship propulsion system in STEAMER (Stevens & Steinberg, 1981) and a power supply in SOPHIE (Brown, Burton, & de Kleer, 1982); (c) completely described procedural rules, for example, strategy learning, WEST (Brown, Burton, & de Kleer, 1982), or arithmetic in BUGGY (Brown & Burton, 1978); (d) partly described procedural rules, for example, computer programming in PROUST (Johnson & Soloway, 1983); LISP Tutor (Anderson, Boyle, & Reiser, 1985); rules in ALGEBRA (McArthur, Stasz, & Hotta, 1987); diagnosis of infectious diseases in GUIDON (Clancey, 1979); and an imperfectly understood complex domain, causes of rainfall in WHY (Stevens, Collins, & Goldin, 1978). Excellent reviews by Barr and Feigenbaum (1982) and Wenger (1987) document many of these ICAI systems. Representative research in ICAI is described by O\u27Neil, Anderson, and Freeman (1986) and Wenger (1987). Although suggestive evidence has been provided by Anderson et al. (1985), few of these ICAI projects have been evaluated in any rigorous fashion. In a sense they have all been toy systems for research and demonstration. Yet, they have raised a good deal of excitement and enthusiasm about their likelihood of being effective instructional environments. With respect to cognitive science, progress has been made in the following areas: identification and analysis of misconceptions or bugs (Clement, Lockhead, & Soloway, 1980), the use of learning strategies (O\u27Neil & Spielberger, 1979; Weinstein & Mayer, 1986), expert versus novice distinction (Chi, Glaser, & Rees, 1982), the role of mental models in learning (Kieras & Bovair, 1983), and the role of self-explanations in problem solving (Chi, Bassok, Lewis, Reimann, & Glaser, 1987). The key components of an ICAI system consist of a knowledge base: that is, (a) what the student is to learn; (b) a student model, either where the student is now with respect to subject matter or how student characteristics interact with subject matters, and (c) a tutor, that is, instructional techniques for teaching the declarative or procedural knowledge. These components are described in more detail by Fletcher (1985)

Dimensions of learning mathematics via technology

Mathematics is a comprehensive, even esthetical experience, affecting a person intellectually, emotionally and physically. The purpose of this study is to determine and examine the dimensions of technology-enhanced mathematics learning. The three learning domains cognitive, psychomotor and affective, ranging from uncomplicated to more complex learning outcomes, as defined by Bloom, have been used a great deal in mathematics pedagogy (Krathwohl, Bloom, & Masia, 1964). This study goes deeper and also examines motivation theory and learning theories when applying technology to the teaching of mathematics. To get a broad picture of the impact of these dimensions on mathematics learning via technology, research was conducted in an array of contexts, including South Af-rica, Mozambique, Germany and Finland. The cross-cultural and cross-countries ap-proach was chosen to ensure wider generalizability of the research. The study invol-ved an action design research (ADR) approach of creating and evaluating artifacts; (i) a novel pedagogical INBECOM model for mathematics learning advocating both behavioristic and constructivist perspectives, and (ii) a newly designed and created story-based UFractions mobile game for learning of fractions incorporating tangible manipulatives. In particular, the affective domain of participants in the study was being studied throughout a ten-year research process from 2009 to 2019. The INBECOM pedagogical model was tested by organizing a fraction course for 21 grade 10 students. The development and evaluation of the pedagogical INBECOM model gives a concrete example of how two learning approaches, constructivism and behaviourism, can be combined in teaching fractions. Furthermore, the results of the qualitative evaluation confirm the view that successful instructional practices have features that are supported by both constructivism and behaviorism. The UFractions mobile game was evaluated with 305 grade 8 students and 12 teachers. Empirical tests indicate that combining concrete manipulatives and mobile phones is a meaningful way to learn the abstract concept of fractions, increasing active student participation. On the basis of the collected data, I initiated a taxonomy for the variety of play motivations in the UFractions game. The dynamics between game motivations and disturbance factors (DF) was analysed. Each motivation relates to a set of DFs typically affecting the player motivation negatively. By becoming aware of these relations, we are able to design more motivating educational games and give guidelines for game developers, users and educators. To explore the affective learning experiences of the three groups of research participants, the qualitative data was derived from the interviews with researchers, teachers and students, as well as from learning diaries, feelings blogs, observations (311 documents) and quantitized (Saldaña, 2009). All the data was explored from the affective perspective, by labelling the feelings the participants experienced according to the affective levels of the Krathwohl et al. (1964) framework. I concluded that affective learning at all five levels was recognized among the three groups of participants. However, the results show that affective learning mostly took place at the receiving level, indicating that the participants received more than they responded, valued, organized or internalized. There was also a significant effect of research participants pertaining to receive; students’ affective learning occurred more at the receiving level than that of the teachers; and teachers’ affective learning emerged more at the value level. Moreover, I define a dimension taxonomy of learning to be used as a framework in the design and implementation of technology-enhanced mathematics teaching and learning including the following three dimensions: (i) Domains of learning, (ii) Orientation of learning, and (iii) Motivation of learning. More precisely, the five domains of learning are cognitive, psychomotor, affective, interpersonal, and intra-personal. Considering orientation of learning, combining behaviorism and constructivism, would lead to more motivating and meaningful teaching and learning strategies. Furthermore, the level of technology integration, the level of students’ cognitive process, and the level of teachers’ knowledge, are intertwined. Motivational fac-tors are an essential part of learning, and it is important to acknowledge connections between motivations and disturbances, when using technology.--- Matematiikka on moniulotteinen kokemus vaikuttaen henkilöön älyllisesti ja tunnetasolla samalla kytkeytyen myös fyysiseen ulottuvuuteen. Tämä tutkimus määrittää ja tarkastelee teknologia-avusteisen matematiikan oppimisen dimensioita. Bloomin määrittämät kolme oppimisen osa-aluetta, kognitiivinen, psykomotorinen ja affektiivinen, jotka etenevät yksinkertaisista monimutkaisempiin oppimisen tasoihin, ovat olleet laajasti käytössä matematiikan pedagogiikassa (Krathwohl, Bloom & Masia, 1964). Tämä tutkimus laajentaa käsitystä oppimisesta tutkimalla motivaatio ja oppimisteorioita sekä niiden käytännön soveltamista matematiikan opetuksessa teknologian avulla. Laajan ymmärryksen saavuttamiseksi siitä, miten nämä tekijät vaikuttavat matematiikan oppimiseen teknologian avulla, tutkimusta toteutettiin monissa eri ympäristöissä, mukaan lukien EteläAfrikka, Mosambik, Saksa ja Suomi. Tutkimuksessa huomioitiin kulttuuriset ja kansainväliset näkökulmat tulosten laajemman yleistettävyyden varmistamiseksi. Tutkimus hyödynsi suunnittelutoimintatutkimuksen (Action Design Research, ADR) menetelmää artefaktien luomiseksi ja evaluoimiseksi: (i) uudenlaista behavioristisia ja konstruktivistisia näkökulmia yhdistävää pedagogista INBECOM-mallia matematiikan oppimiseen, ja (ii) käsinkosketeltavia matematiikan apuvälineitä hyödyntävää UFractions-mobiilipeliä murtolukujen oppimiseen. Erityisesti osallistujien affektiivista oppimista tutkittiin kymmenen vuoden tutkimusprosessin aikana vuosina 2009–2019. INBECOM-pedagogista mallia testattiin järjestämällä murtolukukurssi kansanopiston 10luokalle, jolla oli 21 oppilasta. Pedagogisen INBECOMmallin kehitys ja arviointi antavat konkreettisen esimerkin siitä, miten kahden oppimisteorian, konstruktivismin ja behaviorismin, voi yhdistää murtolukujen opetuksessa. Lisäksi laadullisen arvioinnin tulokset vahvistavat käsitystä siitä, että menestyksellisillä opetusmenetelmillä on piirteitä, jotka hyödyntävät sekä konstruktivistisia että behavioristisia periaatteita. UFractions-mobiilipeli arvioitiin 305 8-luokan opiskelijan ja 12 opettajan avulla. Empiiriset testit osoittavat, että konkreettisten apuvälineiden ja matkapuhelimien yhdistäminen on mielekäs tapa oppia abstrakti murtoluvun käsite ja edistää opiskelijoiden aktiivista osallistumista. Kerätyn datan perusteella kehitettiin taksonomia UFractions-pelin pelimotivaatioista. Pelimotivaatioiden ja häiriötekijöiden (Disturbance Factors, DF) välistä dynamiikkaa analysoitiin. Jokainen motivaatio liittyy tiettyihin häiriötekijöihin, jotka yleensä vaikuttavat pelaajan motivaatioon negatiivisesti. Näiden suhteiden tiedostaminen auttaa suunnittelemaan motivoivampia opetuspelejä ja antaa suuntaviivoja pelikehittäjille, käyttäjille ja opettajille. Affektiivisen oppimisen kokemusten tutkimiseksi tutkimukseen osallistuneiden kolmen ryhmän dataa tarkasteltiin laadullisen tutkimuksen keinoin; tutkijoiden, opettajien ja opiskelijoiden haastattelut, oppimispäiväkirjat, tunneblogi sekä havainnot (311 asiakirjaa) kvantifioitiin (Saldaña, 2009). Kaikki data analysoitiin affektiivisesta näkökulmasta merkitsemällä osallistujien kokemat tunteet Krathwohlin ym. (1964) viitekehyksen affektiivisten tasojen mukaisesti. Tutkimus osoitti, että affektiivista oppimista tunnistettiin kolmen osallistujaryhmän keskuudessa kaikilla viidellä tasolla. Tulokset osoittavat kuitenkin, että affektiivinen oppiminen tapahtui pääasiassa vastaanottotasolla, mikä viittaa siihen, että osallistujat vastaanottivat enemmän kuin he vastasivat, arvostivat, järjestivät tai sisäistivät. Myös osallistujaryhmien affektiivista oppimista koskevat tulokset vaihtelivat merkittävästi: opiskelijoiden affektiivinen oppiminen tapahtui enemmän matalammalla vastaanottotasolla kuin opettajien, ja opettajien affektiivinen oppiminen ilmeni enemmän korkeamman, arvotason oppimisena. Lisäksi tutkimuksessa määritellään oppimisen ulottuvuuksien taksonomia, jota käytetään teknologia-avusteisen matematiikan opetuksen ja oppimisen suunnittelussa ja toteutuksessa. Tähän kuuluu seuraavat kolme ulottuvuutta: (i) Oppimisen osa-alueet, (ii) Oppimisen orientaatio ja (iii) Oppimisen motivaatio. Tarkemmin sanottuna viisi oppimisen osa-aluetta ovat kognitiivinen, psykomotorinen, affektiivinen, interpersonaalinen ja intrapersonaalinen. Yhdistämällä behavioristisia ja konstruktivistisia elementtejä saadaan innostavia ja merkityksellisiä opetus ja oppimisstrategioita. Motivaatiotekijät ovat olennainen osa oppimista, ja teknologiaa käytettäessä on tärkeää tunnistaa yhteydet motivaation ja erilaisten häiriötekijöiden välillä. Lisäksi teknologian integraation taso, opiskelijoiden kognitiivinen prosessi ja opettajien tietotaso ovat kietoutuneet toisiinsa

Facilitating teacher participation in intelligent computer tutor design : tools and design methods.

This work addresses the widening gap between research in intelligent tutoring systems (ITSs) and practical use of this technology by the educational community. In order to ensure that ITSs are effective, teachers must be involved in their design and evaluation. We have followed a user participatory design process to build a set of ITS knowledge acquisition tools that facilitate rapid prototyping and testing of curriculum, and are tailored for usability by teachers. The system (called KAFITS) also serves as a test-bed for experimentation with multiple tutoring strategies. The design includes novel methodologies for tutoring strategy representation (Parameterized Action Networks) and overlay student modeling (a layered student model), and incorporates considerations from instructional design theory. It also allows for considerable student control over the content and style of the information presented. Highly interactive graphics-based tools were built to facilitate design, inspection, and modification of curriculum and tutoring strategies, and to monitor the progress of the tutoring session. Evaluation of the system includes a sixteen-month case study of three educators (one being the domain expert) using the system to build a tutor for statics (forty topics representing about four hours of on-line instruction), testing the tutor on a dozen students, and using test results to iteratively improve the tutor. Detailed throughput analysis indicates that the amount of effort to build the statics tutor was, surprisingly, comparable to similar figures for building (non-intelligent) conventional computer aided instructional systems. Few ITS projects focus on educator participation and this work is the first to empirically study knowledge acquisition for ITSs. Results of the study also include: a recommended design process for building ITSs with educator participation; guidelines for training educators; recommendations for conducting knowledge acquisition sessions; and design tradeoffs for knowledge representation architectures and knowledge acquisition interfaces

Developing personalized education. A dynamic framework

Personalized education—the systematic adaptation of instruction to individual learners—has been a long-striven goal. We review research on personalized education that has been conducted in the laboratory, in the classroom, and in digital learning environments. Across all learning environments, we find that personalization is most successful when relevant learner characteristics are measured repeatedly during the learning process and when these data are used to adapt instruction in a systematic way. Building on these observations, we propose a novel, dynamic framework of personalization that conceptualizes learners as dynamic entities that change during and in interaction with the instructional process. As these dynamics manifest on different timescales, so do the opportunities for instructional adaptations—ranging from setting appropriate learning goals at the macroscale to reacting to affective-motivational fluctuations at the microscale. We argue that instructional design needs to take these dynamics into account in order to adapt to a specific learner at a specific point in time. Finally, we provide some examples of successful, dynamic adaptations and discuss future directions that arise from a dynamic conceptualization of personalization. (DIPF/Orig.