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

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

Managing affect in learners' questions in undergraduate science

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 Society for Research into Higher Education.This article aims to position students' classroom questioning within the literature surrounding affect and its impact on learning. The article consists of two main sections. First, the act of questioning is discussed in order to highlight how affect shapes the process of questioning, and a four-part genesis to question-asking that we call CARE is described: the construction, asking, reception and evaluation of a learner's question. This work is contextualised through studies in science education and through our work with university students in undergraduate chemistry, although conducted in the firm belief that it has more general application. The second section focuses on teaching strategies to encourage and manage learners' questions, based here upon the conviction that university students in this case learn through questioning, and that an inquiry-based environment promotes better learning than a simple ‘transmission’ setting. Seven teaching strategies developed from the authors' work are described, where university teachers ‘scaffold’ learning through supporting learners' questions, and working with these to structure and organise the content and the shape of their teaching. The article concludes with a summary of the main issues, highlighting the impact of the affective dimension of learning through questioning, and a discussion of the implications for future research

New measurement paradigms

This collection of New Measurement Paradigms papers represents a snapshot of the variety of measurement methods in use at the time of writing across several projects funded by the National Science Foundation (US) through its REESE and DR K–12 programs. All of the projects are developing and testing intelligent learning environments that seek to carefully measure and promote student learning, and the purpose of this collection of papers is to describe and illustrate the use of several measurement methods employed to achieve this. The papers are deliberately short because they are designed to introduce the methods in use and not to be a textbook chapter on each method. The New Measurement Paradigms collection is designed to serve as a reference point for researchers who are working in projects that are creating e-learning environments in which there is a need to make judgments about students’ levels of knowledge and skills, or for those interested in this but who have not yet delved into these methods

Natural Language Tutoring and the Novice Programmer

For beginning programmers, inadequate problem solving and planning skills are among the most salient of their weaknesses. Novices, by definition, lack much of the tacit knowledge that underlies effective programming. This dissertation examines the efficacy of natural language tutoring (NLT) to foster acquisition of this tacit knowledge. Coached Program Planning (CPP) is proposed as a solution to the problem of teaching the tacit knowledge of programming. The general aim is to cultivate the development of such knowledge by eliciting and scaffolding the problem solving and planning activities that novices are known to underestimate or bypass altogether. ProPL (pro-PELL), a dialogue-based intelligent tutoring system based on CPP, is also described. In an evaluation, the primary findings were that students who received tutoring from ProPL seemed to exhibit an improved ability compose plans and displayed behaviors suggestive of thinking at greater levels of abstraction than students in a read-only control group. The major finding is that NLT appears to be effective in teaching program composition skills

Effective Tutoring with Empathic Embodied Conversational Agents

This thesis examines the prospect of using empathy in an Embodied Tutoring System (ETS) that guides students through an online quiz (by providing feedback on student answers and responding to self-reported student emotion). The ETS seeks to imitate human behaviours successfully used in one-to-one human tutorial interactions. The main hypothesis is that the interaction with an empathic ETS results in greater learning gains than a neutral ETS, primarily by encouraging positive and reducing negative student emotions using empathic feedback. In a preparatory study we investigated different strategies for expressing emotion by the ETS. We established that a multimodal strategy achieves the best results regarding how accurately human participants can recognise the emotions. This approach was used in developing the feedback strategy for our empathic ETS. The preparatory study was followed by two studies in which we compared a neutral with an empathic ETS. The ETS in the second of these studies was developed using results from the first of these studies. In both studies, we found no statistically significant difference in learning gains between the neutral and empathic ETS. However, we did discover a number of interactions between the ETS system, learning gains and, in particular 1) student scores on an empathic tendency test and 2) student ability. We also analysed the subjective responses and the relation between self-reported emotions during the quiz and student learning gains. Based on our studies in a formal class room setting, we assess the prospects of using empathic agents in a classroom setting and describe a number of requirements for their effective use

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora

Generating explanatory discourse: a plan-based, interactive approach

SIGLEAvailable from British Library Document Supply Centre- DSC:D91660 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

TOWARDS BUILDING INTELLIGENT COLLABORATIVE PROBLEM SOLVING SYSTEMS

Historically, Collaborative Problem Solving (CPS) systems were more focused on Human Computer Interaction (HCI) issues, such as providing good experience of communication among the participants. Whereas, Intelligent Tutoring Systems (ITS) focus both on HCI issues as well as leveraging Artificial Intelligence (AI) techniques in their intelligent agents. This dissertation seeks to minimize the gap between CPS systems and ITS by adopting the methods used in ITS researches. To move towards this goal, we focus on analyzing interactions with textual inputs in online learning systems such as DeepTutor and Virtual Internships (VI) to understand their semantics and underlying intents. In order to address the problem of assessing the student generated short text, this research explores firstly data driven machine learning models coupled with expert generated as well as general text analysis features. Secondly it explores method to utilize knowledge graph embedding for assessing student answer in ITS. Finally, it also explores a method using only standard reference examples generated by human teacher. Such method is useful when a new system has been deployed and no student data were available.To handle negation in tutorial dialogue, this research explored a Long Short Term Memory (LSTM) based method. The advantage of this method is that it requires no human engineered features and performs comparably well with other models using human engineered features.Another important analysis done in this research is to find speech acts in conversation utterances of multiple players in VI. Among various models, a noise label trained neural network model performed better in categorizing the speech acts of the utterances.The learners\u27 professional skill development in VI is characterized by the distribution of SKIVE elements, the components of epistemic frames. Inferring the population distribution of these elements could help to assess the learners\u27 skill development. This research sought a Markov method to infer the population distribution of SKIVE elements, namely the stationary distribution of the elements.While studying various aspects of interactions in our targeted learning systems, we motivate our research to replace the human mentor or tutor with intelligent agent. Introducing intelligent agent in place of human helps to reduce the cost as well as scale up the system