Showing papers on "Intelligent tutoring system published in 2012"

Cognitively diagnostic assessment

Abstract: Contents: Preface. S.F. Chipman, P.D. Nichols, R.L. Brennan, Introduction. A.T. Corbett, J.R. Anderson, A.T. O'Brien, Student Modeling in the ACT Programming Tutor. R.J. Mislevy, Probability-Based Inference in Cognitive Diagnosis. D.H. Gitomer, L.S. Steinberg, R.J. Mislevy, Diagnostic Assessment of Troubleshooting Skill in an Intelligent Tutoring System. K.L. Draney, P. Pirolli, M. Wilson, A Measurement Model for a Complex Cognitive Skill. T.A. Polk, K. VanLehn, D. Kalp, ASPM2: Progress Toward the Analysis of Symbolic Parameter Models. J. Martin, K. VanLehn, A Bayesian Approach to Cognitive Assessment. G. Biswas, S.R. Goldman, D. Fisher, B. Bhuva, G. Glewwe, Assessing Design Activity in Complex CMOS Circuit Design. D. DuBois, V.L. Shalin, Adapting Cognitive Methods to Real-World Objectives: An Application to Job Knowledge Testing. P.J. Johnson, T.E. Goldsmith, K.W. Teague, Similarity, Structure, and Knowledge: A Representational Approach to Assessment. B.K. Britton, P. Tidwell, Cognitive Structure Testing: A Computer System for Diagnosis of Expert-Novice Differences. M. Naveh-Benjamin, Y-G. Lin, W.J. McKeachie, Inferring Students' Cognitive Structures and Their Development Using the "Fill-in-the-Structure" (FITS) Technique. J.E. Corter, Using Clustering Methods to Explore the Structure of Diagnostic Tests. K.K. Tatsuoka, Architecture of Knowledge Structures and Cognitive Diagnosis: A Statistical Pattern Recognition and Classification Approach. L.V. DiBello, W.F. Stout, L.A. Roussos, Unified Cognitive/Psychometric Diagnostic Assessment Likelihood-Based Classification Techniques. F. Samejima, A Cognitive Diagnosis Method Using Latent Trait Models: Competency Space Approach and Its Relationship With DiBello and Stout's Unified Cognitive-Psychometric Diagnosis Model. E. Hunt, Where and When to Represent Students This Way and That Way: An Evaluation of Approaches to Diagnostic Assessment. S.P. Marshall, Some Suggestions for Alternative Assessments.

Gaze tutor: A gaze-reactive intelligent tutoring system

Abstract: We developed an intelligent tutoring system (ITS) that aims to promote engagement and learning by dynamically detecting and responding to students' boredom and disengagement. The tutor uses a commercial eye tracker to monitor a student's gaze patterns and identify when the student is bored, disengaged, or is zoning out. The tutor then attempts to reengage the student with dialog moves that direct the student to reorient his or her attentional patterns towards the animated pedagogical agent embodying the tutor. We evaluated the efficacy of the gaze-reactive tutor in promoting learning, motivation, and engagement in a controlled experiment where 48 students were tutored on four biology topics with both gaze-reactive and non-gaze-reactive (control condition) versions of the tutor. The results indicated that: (a) gaze-sensitive dialogs were successful in dynamically reorienting students' attentional patterns to the important areas of the interface, (b) gaze-reactivity was effective in promoting learning gains for questions that required deep reasoning, (c) gaze-reactivity had minimal impact on students' state motivation and on self-reported engagement, and (d) individual differences in scholastic aptitude moderated the impact of gaze-reactivity on overall learning gains. We discuss the implications of our findings, limitations, future work, and consider the possibility of using gaze-reactive ITSs in classrooms.

A conversational intelligent tutoring system to automatically predict learning styles

Abstract: This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100% Participants also found Oscar's tutoring helpful and achieved an average learning gain of 13%

Confusion and complex learning during interactions with computer learning environments

Abstract: Folk wisdom holds that being confused is detrimental to learning. However, research on emotions and learning suggest a somewhat more complex relationship between confusion and learning outcomes. In fact, it has been proposed that impasses that trigger states of cognitive disequilibrium and confusion can create opportunities for deep learning of conceptually difficult content. This paper discusses four computer learning environments that either naturally or artificially induce confusion in learners in order to create learning opportunities. First, an Intelligent Tutoring System called AutoTutor that engenders confusion through challenging problems and vague hints is described. The remaining three environments were specifically designed to induce confusion through a number of different interventions. These interventions include device breakdowns, contradictory information, and false feedback. The success and limitations of confusion induction and the impact of confusion resolution on learning are discussed. Potential methods to help learners productively manage their confusion instead of being hopelessly confused are also discussed.

Predicting learners performance using artificial neural networks in linear programming intelligent tutoring system

Abstract: In this paper we present a technique that employ Artificial Neural Networks and expert systems to obtain knowledge for the learner model in the Linear Programming Intelligent Tutoring System(LP-ITS) to be able to determine the academic performance level of the learners in order to offer him/her the proper difficulty level of linear programming problems to solve. LP-ITS uses Feed forward Back-propagation algorithm to be trained with a group of learners data to predict their academic performance. Furthermore, LP-ITS uses an Expert System to decide the proper difficulty level that is suitable with the predicted academic performance of the learner. Several tests have been carried out to examine adherence to real time data. The accuracy of predicting the performance of the learners is very high and thus states that the Artificial Neural Network is skilled enough to make suitable predictions.

Guru: a computer tutor that models expert human tutors

Abstract: We present Guru, an intelligent tutoring system for high school biology that has conversations with students, gestures and points to virtual instructional materials, and presents exercises for extended practice. Guru's instructional strategies are modeled after expert tutors and focus on brief interactive lectures followed by rounds of scaffolding as well as summarizing, concept mapping, and Cloze tasks. This paper describes the Guru session and presents learning outcomes from an in-school study comparing Guru, human tutoring, and classroom instruction. Results indicated significant learning gains for students in the Guru and human tutoring conditions compared to classroom controls.

Student modeling and assessment in intelligent tutoring of software patterns

Abstract: This paper presents the design, implementation, and evaluation of a student model in DEPTHS (Design Pattern Teaching Help System), an intelligent tutoring system for learning software design patterns. There are many approaches and technologies for student modeling, but choosing the right one depends on intended functionalities of an intelligent system that the student model is going to be used in. Those functionalities often determine the kinds of information that the student model should contain. The student model used in DEPTHS is a result of combining two widely known modeling approaches, namely, stereotype and overlay modeling. The model is domain independent and can be easily applied in other learning domains as well. To keep student model update during the learning process, DEPTHS makes use of a knowledge assessment method based on fuzzy rules (i.e., a combination of production rules and fuzzy logics). The evaluation of DEPTHS performed with the aim of assessing the system's overall effectiveness and the accuracy of its student model, indicated several advantages of the DEPTHS system over the traditional approach to learning design patterns, and encouraged us to move on further with this research.

Large-scale randomized controlled trial with 4th graders using intelligent tutoring of the structure strategy to improve nonfiction reading comprehension

Abstract: Reading comprehension is a challenge for K-12 learners and adults. Nonfiction texts, such as expository texts that inform and explain, are particularly challenging and vital for students’ understanding because of their frequent use in formal schooling (e.g., textbooks) as well as everyday life (e.g., newspapers, magazines, and medical information). The structure strategy is explicit instruction about how to strategically use knowledge about text structures for encoding and retrieval of information from nonfiction and has consistently shown significant improvements in reading comprehension. We present the delivery of the structure strategy using a web-based intelligent tutoring system (ITSS) that has the potential to offer consistent modeling, practice tasks, assessment, and feedback to the learner. Finally, we report on statistically significant findings from a large scale randomized controlled efficacy trial with rural and suburban 4th-grade students using ITSS.

The Effects of an Interactive Software Agent on Student Affective Dynamics while Using ;an Intelligent Tutoring System

Abstract: We study the affective states exhibited by students using an intelligent tutoring system for Scatterplots with and without an interactive software agent, Scooter the Tutor. Scooter the Tutor had been previously shown to lead to improved learning outcomes as compared to the same tutoring system without Scooter. We found that affective states and transitions between affective states were very similar among students in both conditions. With the exception of the "neutral state,” no affective state occurred significantly more in one condition over the other. Boredom, confusion, and engaged concentration persisted in both conditions, representing both "virtuous cycles” and "vicious cycles” that did not appear to differ by condition. These findings imply that - although Scooter is well liked by students and improves student learning outcomes relative to the original tutor - Scooter does not have a large effect on students' affective states or their dynamics.

"Oh dear stacy!": social interaction, elaboration, and learning with teachable agents

Abstract: Understanding how children perceive and interact with teachable agents (systems where children learn through teaching a synthetic character embedded in an intelligent tutoring system) can provide insight into the effects of so-cial interaction on learning with intelligent tutoring systems. We describe results from a think-aloud study where children were instructed to narrate their experience teaching Stacy, an agent who can learn to solve linear equations with the student's help. We found treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or we rather than she or it significantly correlates with student learning, as do playful face-threatening comments such as teasing, while elaborate explanations of Stacy's behavior in the third-person and formal tutoring statements reduce learning gains. Additionally, we found that the agent's mistakes were a significant predictor for students shifting away from alignment with the agent.

Language and Discourse Are Powerful Signals of Student Emotions during Tutoring

Abstract: We explored the possibility of predicting student emotions (boredom, flow/engagement, confusion, and frustration) by analyzing the text of student and tutor dialogues during interactions with an Intelligent Tutoring System (ITS) with conversational dialogues. After completing a learning session with the tutor, student emotions were judged by the students themselves (self-judgments), untrained peers, and trained judges. Transcripts from the tutorial dialogues were analyzed with four methods that included 1) identifying direct expressions of affect, 2) aligning the semantic content of student responses to affective terms, 3) identifying psychological and linguistic terms that are predictive of affect, and 4) assessing cohesion relationships that might reveal student affect. Models constructed by regressing the proportional occurrence of each emotion on textual features derived from these methods yielded large effects (R2 = 38%) for the psychological, linguistic, and cohesion-based methods, but not the direct expression and semantic alignment methods. We discuss the theoretical, methodological, and applied implications of our findings toward text-based emotion detection during tutoring.

Evaluating the integration of fuzzy logic into the student model of a web-based learning environment

Abstract: In this paper, we evaluate the effectiveness and accuracy of the student model of a web-based educational environment for teaching computer programming. Our student model represents the learner's knowledge through an overlay model and uses a fuzzy logic technique in order to define and update the student's knowledge level of each domain concept, each time that s/he interacts with the e-learning system. Evaluation of the student model of an Intelligent Tutoring System (ITS) is an aspect for which there are not clear guidelines to be provided by literature. Therefore, we choose to use two well-known evaluation methods for the evaluation of our fuzzy student model, in order to design an accurate and correct evaluation methodology. These evaluation models are: the Kirkpatrick's model and the layered evaluation method. Our system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus, in order to learn how to program in the programming language C. The results of the evaluation were very encouraging.

An adaptive and innovative question-driven competition-based intelligent tutoring system for learning

Abstract: ISCARE (Information System for Competition based on pRoblem solving in Education) is a new and innovative intelligent tutoring system that we have designed and implemented. This tool allows the competition among students for improving their learning process in a course. The tool takes some ideas from the Swiss-system widely used in chess and adapts them to the educational area. The competition is based on different tournaments and rounds. In each round, students are assigned in pairs of two, which compete one against another, and each pair receives different questions that students have to solve in a limit of time. Students can see their partial ratings after each round and their final rating after a tournament. A lot of knowledge from different disciplines was used to design, and implement this system, as ISCARE includes different functionality such as the students' registration into the system, the creation of tournaments, the registration and assignment of students to tournaments, the management of each tournament life cycle (started, in execution, finished, etc.), the addition of the different exercises to tournaments, the calculation of pairs of students for each round with different algorithms, the assignment of exercises per round and pair, the scorings of the students per round and tournament, the management of the students' ratings, or the visualization of information. This paper presents the ISCARE intelligent tutoring system, describing its different options, menus, or functionality as well as its architecture and the specific modeling to achieve the desired features.

An optimal assessment of natural language student input using word-to-word similarity metrics

Abstract: We address in this paper the important task of assessing natural language student input in dialogue-based intelligent tutoring systems. Student input, in the form of dialogue turns called contributions must be understood in order to build an accurate student model which in turn is important for providing adequate feedback and scaffolding. We present a novel, optimal semantic similarity approach based on word-to-word similarity metrics and compare it with a greedy method as well as with a baseline method on one data set from the intelligent tutoring system, AutoTutor.

An Ontology-Based Model for Student Representation in Intelligent Tutoring Systems for Distance Learning

Abstract: An Intelligent Tutoring System (ITS) offers personalized education to each student in accordance with his/her learning preferences and his/her background. One of the most fundamental components of an ITS is the student model, that contains all the information about a student such as demographic information, learning style and academic performance. This information enables the system to be fully adapted to the student. Our research work intends to propose a student model and enhance it with semantics by developing (or via) an ontology in order to be exploitable effectively within an ITS, for example as a domain-independent vocabulary for the communication between intelligent agents. The ontology schema consists of two main taxonomies: (a) student’s academic information and (b) student’s personal information. The characteristics of the student that have been included in the student model ontology were derived from an empirical study on a sample of students.

Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning

Abstract: Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students learning about a complex science topic with an agent-based ITS that fosters self-regulated learning (SRL). Preliminary analysis with an Expectation-Maximization clustering algorithm revealed the existence of three distinct groups of students, distinguished by their test and quiz scores (low for the first group, medium for the second group, and high for the third group), their learning gains (low, medium, high), the frequency of their note-taking (rare, frequent, rare) and note-checking (rare, rare, frequent), the proportion of sub-goals attempted (low, low, high), and the time spent reading (high, high, low). In this paper, we extend this analysis to identify characteristic learning behaviors and strategies that distinguish these three groups of students. We employ a differential sequence mining technique to identify differentially frequent activity patterns between the student groups and interpret these patterns in terms of relevant learning behaviors. The results of this analysis reveal that high-performing students tend to be better at quickly identifying the relevance of a page to their subgoal, are more methodical in their exploration of the pedagogical content, rely on system prompts to take notes and summarize, and are more strategic in their preparation for the post-test (e.g., using the end of their session to briefly review pages). These results provide a first step in identifying the group to which a student belongs during the learning session, thus making possible a real-time adaptation of the system.

A Qualitative Study of LP-ITS: Linear Programming Intelligent Tutoring System

Abstract: This paper is an attempt to evaluate the Linear Programming Intelligent Tutoring System on the basis of perspective and experiences of instructors and students who used the system in the Faculty of Engineering & Information Technology at Al-Azhar University in Gaza. A phenomenological method, with a focal point group was used. The first objective of this study was to discuss the important aspects of the design and development of LP-ITS. The second was to evaluate LP-ITS on the basis of instructors and students experiences. The third was to explore the perspectives of students and instructors about the implication of LP-ITS skills in lecture hall situations. The results were discussed in terms of the evaluation of the LP-ITS and its implications for learning and teaching activities in the lecture hall.

Passively Classifying Student Mood and Performance within Intelligent Tutors

Abstract: It has been long recognized that successful human tutors are capable of adapting instruction to mitigate barriers (e.g., withdrawal or frustration) to learning during the one-to-one tutoring process. A significant part of the success of human tutors is based on their perception of student affect (e.g., mood or emotions). To at least match the capabilities of human tutors, computer-based intelligent tutoring system (ITS) will need to “perceive” student affect and improve performance by selecting more effective instructional strategies (e.g., feedback). To date, ITS have fallen short in realizing this capability. Much of the existing research models the emotions of virtual characters rather than assessing the affective state of the student. Our goal was to determine the context and importance of student mood in an adaptable ITS model. To enhance our existing model, we evaluated procedural reasoning systems used in virtual characters, and reviewed behavioral and physiological sensing methods and predictive models of affect. Our experiment focused on passive capture of behaviors (e.g., mouse movement) during training to predict the student’s mood. The idea of mood as a constant during training and predictors of performance are also discussed.

A paradigm for handwriting-based intelligent tutors

Abstract: This paper presents the interaction design of, and demonstration of technical feasibility for, intelligent tutoring systems that can accept handwriting input from students. Handwriting and pen input offer several affordances for students that traditional typing-based interactions do not. To illustrate these affordances, we present evidence, from tutoring mathematics, that the ability to enter problem solutions via pen input enables students to record algebraic equations more quickly, more smoothly (fewer errors), and with increased transfer to non-computer-based tasks. Furthermore our evidence shows that students tend to like pen input for these types of problems more than typing. However, a clear downside to introducing handwriting input into intelligent tutors is that the recognition of such input is not reliable. In our work, we have found that handwriting input is more likely to be useful and reliable when context is considered, for example, the context of the problem being solved. We present an intelligent tutoring system for algebra equation solving via pen-based input that is able to use context to decrease recognition errors by 18% and to reduce recognition error recovery interactions to occur on one out of every four problems. We applied user-centered design principles to reduce the negative impact of recognition errors in the following ways: (1) though students handwrite their problem-solving process, they type their final answer to reduce ambiguity for tutoring purposes, and (2) in the small number of cases in which the system must involve the student in recognition error recovery, the interaction focuses on identifying the student's problem-solving error to keep the emphasis on tutoring. Many potential recognition errors can thus be ignored and distracting interactions are avoided. This work can inform the design of future systems for students using pen and sketch input for math or other topics by motivating the use of context and pragmatics to decrease the impact of recognition errors and put user focus on the task at hand.

Game-Based Practice in a Reading Strategy Tutoring System: Showdown in iSTART-ME

Abstract: Many contend that the future of affordable, high-quality education lies in harnessing the potential of computer technologies. While implementing computer technologies in schools has had both failings and challenges (Dynarski et al., 2007), significant progress in the quality of education to some extent depends on our ability to leverage the many advantages of computer technologies. Computer technologies enable adaptive, one-on-one tutoring to virtually all students in the classroom. The most common goal of these one-on-one intelligent tutoring systems (ITSs) is to produce learning gains. Two of the most common areas of learning address content within specific domains (e.g., physics) or cognitive skill acquisition (e.g., strategies to improve reading comprehension). Both types of learning are often characterized by exposure to declarative information and subsequent interaction with the material (Anderson, 1982). However, acquiring a new skill usually requires a significant commitment to continued practice and application. Skills are often developed and improved with practice over an extended period of time (Newell & Rosenbloom, 1981).

PhysicsBook: a sketch-based interface for animating physics diagrams

Abstract: We present PhysicsBook, a prototype system that enables users to solve physics problems using a sketch-based interface and then animates any diagram used in solving the problem to show that the solution is correct. PhysicsBook recognizes the diagrams in the solution and infers relationships among diagram components through the recognition of mathematics and annotations such as arrows and dotted lines. For animation, PhysicsBook uses a customized physics engine that provides entry points for hand-written mathematics and diagrams. We discuss the design of PhysicsBook, including details of algorithms for sketch recognition, inference of user intent and creation of animations based on the mathematics written by a user. Specifically, we describe how the physics engine uses domain knowledge to perform data transformations in instances where it cannot use a given equation directly. This enables PhysicsBook to deal with domains of problems that are not directly related to classical mechanics. We provide examples of scenarios of how PhysicsBook could be used as part of an intelligent tutoring system and discuss the strengths and weaknesses of our current prototype. Lastly, we present the findings of a preliminary usability study with five participants.

Learner Differences in Hint Processing.

Abstract: Although ITSs are supposed to adapt to differences among learners, so far, little attention has been paid to how they might adapt to differences in how students learn from help. When students study with an Intelligent Tutoring System, they may receive multiple types of help, but may not comprehend and make use of this help in the same way. To measure the extent of such individual differences, we propose two new logistic regression models, ProfHelp and ProfHelp-ID. Both models extend the Performance Factors Analysis model (Pavlik, Cen & Koedinger, 2009) with parameters that represent the effect of hints on performance on the same step on which the help was given. Both models adjust for general student proficiency, prior practice on knowledge components, and knowledge component difficulty. Multilevel Bayesian implementations of these models were fit to data on student interactions with a geometry ITS, where students received on-demand problem-relevant help ranging from firstlevel hints that facilitate application of principles to specific and immediately actionable bottom-out hints. The model comparison showed that in this dataset students differ in their individual hintprocessing proficiency and these differences depend on hint levels. These results suggest that we can assess specific learning skills, e.g., making sense of instructional text, and in future work we may be able to remediate and improve such skills.

A Fuzzy Expert System Architecture for Intelligent Tutoring Systems: A Cognitive Mapping Approach

Abstract: An Intelligent Tutoring System (ITS) is a computer based instruction tool that attempts to provide individualized instructions based on learner’s educational status. Advances in development of these systems have rose and fell since their emergence. Perhaps the main reason for this is the absence of appropriate framework for ITS development. This paper proposes a framework for designing two main parts of ITSs. Besides development framework, the second main reason for lack of significant advances in ITS development is its development cost. In general, this cost for instructional material is quite high and it becomes more in ITS development. The proposed method can significantly reduce the development cost. The cost reduction mainly is because of characteristics of applied mapping techniques. These maps are human readable and easily understandable by people who are not aware of knowledge representation techniques. The proposed framework is implemented for a graduate course at a technical university in Asia. This experiment provides an individualized instruction which is the main designing purpose of the ITSs.

Virtual industrial training: Joining innovative interfaces with plant modeling

Abstract: Training in industry is one of the most critical and expensive tasks to be faced by the management. Furthermore, in some cases, it is dangerous or even impossible to directly train operators on the real plants where security and safety problems may arise, making it very difficult to start training programs at low cost. For these reasons, the field of training in industry is rapidly developing using software or hardware solutions coming mainly from the following research areas: i) Human-Computer interaction, i.e., the use of complex and interactive human-machine interfaces, ii) plant simulators, i.e., software systems which are delivered with the plant itself to test and to learn complex tasks and processes, iii) Intelligent Training Systems, i.e., the availability of intelligent and personalized training systems where a virtual tutor guides users through a personalized learning path. In this paper we present the overall architecture of a system for industrial training, embedded into an Intelligent Tutoring System that can provide more effective and personalized training and learning in a context where working directly on real plants can be difficult and very expensive. In particular we present a simulator for training operators in using power plants, based on a multimedia and on interactive interface. This system is particularly suitable to be used for training in industrial electric and oil plants. Moreover, the system allows operators for collaborative problem solving. Currently the system is under delivery to an Italian Electric industry.

BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-Form Student Input.

Abstract: This dissertation describes a novel intelligent tutoring system, BeSocratic, which aims to help fill the gap between simple multiple-choice systems and free-response systems. BeSocratic focuses on targeting questions that are free-form in nature yet defined to the point which allows for automatic evaluation and analysis. The system includes a set of modules which provide instructors with tools to assess student performance. Beyond text boxes and multiple-choice questions, BeSocratic contains several modules that recognize, evaluate, provide feedback, and analyze studentdrawn structures, including Euclidean graphs, chemistry molecules, computer science graphs, and simple drawings. Our system uses a visual, rule-based authoring system which enables the creation of activities for use within science, technology, engineering, and mathematics classrooms. BeSocratic records each action that students make within the system. Using a set of postanalysis tools, teachers have the ability to examine both individual and group performances. We accomplish this using hidden Markov model-based clustering techniques and visualizations. These visualizations can help teachers quickly identify common strategies and errors for large groups of students. Furthermore, analysis results can be used directly to improve activities through advanced detection of student errors and refined feedback. BeSocratic activities have been created and tested at several universities. We report specific results from several activities, and discuss how BeSocratic’s analysis tools are being used with data from other systems. We specifically detail two chemistry activities and one computer science activity: (1) an activity focused on improving mechanism use, (2) an activity which assesses student understanding of Gibbs energy, and (3) an activity which teaches students the fundamentals of splay trees. In addition to analyzing data collected from students within BeSocratic, we share our visualizations and results from analyzing data gathered with another educational system, PhET.

Employing Textual and Facial Emotion Recognition to Design an Affective Tutoring System.

Abstract: Emotional expression in Artificial Intelligence has gained lots of attention in recent years, people applied its affective computing not only in enhancing and realizing the interaction between computers and human, it also makes computer more humane. In this study, emotional expressions were applied into intelligent tutoring system, where learners’ emotional expression in learning process was observed in order to give an appropriate feedback. Emotional intelligent not only gives high flexibility to the interaction of tutoring system, it also to deepen its level of human interaction. This study uses dual-mode operation: facial expression recognition, and text semantics as the main elements in affective computing to understand users’ emotions. Text semantics are used to understand learner’s learning status, and the results would contribute to course management agents in order to choose the most appropriate teaching strategies and feedback to the users. Facial expression recognition allows interactive agents to provide users a complete sound and animation feedback..

Feedback Provision Strategies in Intelligent Tutoring Systems Based on Clustered Solution Spaces

Abstract: Designing an Intelligent Tutoring System (ITS) usually requires precise models of the underlying domain, as well as of how a human tutor would respond to student mistakes. As such, the applicability of ITSs is typically restricted to welldefined domains where such a formalization is possible. The extension of ITSs to ill-defined domains constitutes a challenge. In this paper, we propose the provision of feedback based on solution spaces which are automatically clustered by machine learning techniques operating on sets of student solutions. We validated our approach in an expert evaluation with a data set from a programming course. The evaluation confirmed the feasibility of the proposed feedback provision strategies.

Ask-Elle: a haskell tutor demonstration

Abstract: In this demonstration we will introduce Ask-Elle, a Haskell tutor. Ask-Elle supports the incremental development of Haskell programs. It can give hints on how to proceed with solving a programming exercise, and feedback on incomplete student programs. We will show Ask-Elle in action, and discuss how a teacher can configure its behaviour.