Showing papers on "Intelligent tutoring system published in 2008"

The politeness effect: Pedagogical agents and learning outcomes

Abstract: Pedagogical agent research seeks to exploit Reeves and Nass's media equation theory, which holds that users respond to interactive media as if they were social actors. Investigations have tended to focus on the media used to realize the pedagogical agent, e.g., the use of animated talking heads and voices, and the results have been mixed. This paper focuses instead on the manner in which a pedagogical agent communicates with learners, i.e., on the extent to which it exhibits social intelligence. A model of socially intelligent tutorial dialog was developed based on politeness theory, and implemented in an agent interface within an online learning system called virtual factory teaching system. A series of Wizard-of-Oz studies was conducted in which subjects either received polite tutorial feedback that promotes learner face and mitigates face threat, or received direct feedback that disregards learner face. The polite version yielded better learning outcomes, and the effect was amplified in learners who expressed a preference for indirect feedback, who had less computer experience, and who lacked engineering backgrounds. These results confirm the hypothesis that learners tend to respond to pedagogical agents as social actors, and suggest that research should focus less on the media in which agents are realized, and place more emphasis on the agent's social intelligence.

Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive–affective states during learning

Abstract: In an attempt to discover the facial action units for affective states that occur during complex learning, this study adopted an emote-aloud procedure in which participants were recorded as they verbalised their affective states while interacting with an intelligent tutoring system (AutoTutor). Participants’ facial expressions were coded by two expert raters using Ekman's Facial Action Coding System and analysed using association rule mining techniques. The two expert raters received an overall kappa that ranged between .76 and .84. The association rule mining analysis uncovered facial actions associated with confusion, frustration, and boredom. We discuss these rules and the prospects of enhancing AutoTutor with non-intrusive affect-sensitive capabilities.

Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data

Abstract: We have proposed a novel application of Markov decision processes (MDPs), a reinforcement learning technique, to automatically generate hints for an intelligent tutor that learns. We demonstrate the feasibility of this approach by extracting MDPs from four semesters of student solutions in a logic proof tutor, and calculating the probability that we will be able to generate hints at any point in a given problem. Our results indicate that extracted MDPs and our proposed hint-generating functions will be able to provide hints over 80% of the time. Our results also indicate that we can provide valuable tradeoffs between hint specificity and the amount of data used to create an MDP.

CALMsystem: A Conversational Agent for Learner Modelling

Abstract: This paper describes a system which incorporates natural language technologies, database manipulation and educational theories in order to offer learners a Negotiated Learner Model, for integration into an Intelligent Tutoring System. The system presents the learner with their learner model, offering them the opportunity to compare their own beliefs regarding their capabilities with those inferred by the system. A conversational agent, or ''chatbot'' has been developed to allow the learner to negotiate over the representations held about them using natural language. The system aims to support the metacognitive goals of self-assessment and reflection, which are increasingly seen as key to learning and are being incorporated into UK educational policy. The paper describes the design of the system, and reports a user trial, in which the chatbot was found to support users in increasing the accuracy of their self-assessments, and in reducing the number of discrepancies between system and user beliefs in the learner model. Some lessons learned in the development have been highlighted and future research and experimentation directions are outlined.

Personalized web-based tutoring system based on fuzzy item response theory

Abstract: With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field. Previously, many researchers put effort into e-learning systems with personalized learning mechanism to aid on-line learning. However, most systems focus on using learner's behaviors, interests, and habits to provide personalized e-learning services. These systems commonly neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other. Frequently, unsuitable courseware causes learner's cognitive overload or disorientation during learning. To promote learning effectiveness, our previous study proposed a personalized e-learning system based on Item response theory (PEL-IRT), which can consider both course material difficulty and learner ability evaluated by learner's crisp feedback responses (i.e. completely understanding or not understanding answer) to provide personalized learning paths for individual learners. The PEL-IRT cannot estimate learner ability for personalized learning services according to learner's non-crisp responses (i.e. uncertain/fuzzy responses). The main problem is that learner's response is not usually belonging to completely understanding or not understanding case for the content of learned courseware. Therefore, this study developed a personalized intelligent tutoring system based on the proposed fuzzy item response theory (FIRT), which could be capable of recommending courseware with suitable difficulty levels for learners according to learner's uncertain/fuzzy feedback responses. The proposed FIRT can correctly estimate learner ability via the fuzzy inference mechanism and revise estimating function of learner ability while the learner responds to the difficulty level and comprehension percentage for the learned courseware. Moreover, a courseware modeling process developed in this study is based on a statistical technique to establish the difficulty parameters of courseware for the proposed personalized intelligent tutoring system. Experiment results indicate that applying the proposed FIRT to web-based learning can provide better learning services for individual learners than our previous study, thus helping learners to learn more effectively.

TEx-Sys model for building intelligent tutoring systems

Abstract: Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student's characteristics. Authoring shells have an environment that enables development of the intelligent tutoring systems. In this paper we present, in entirety, for the first time, our approach to research, development and implementation related to intelligent tutoring systems and ITS authoring shells. Our research relies on the traditional intelligent tutoring system, the consideration that teaching is control of learning and principles of good human tutoring in order to develop the Tutor-Expert System model for building intelligent tutoring systems in freely chosen domain knowledge. In this way we can wrap up an ongoing process that has lasted for the previous fifteen years. Prototype tests with the implemented systems have been carried out with students from a primary education to an academic level. Results of those tests are advantageous, according to surveys, and the implemented and deployed software satisfies functionalities and actors' demands.

A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems

Abstract: Domain experts should provide relevant domain knowledge to an Intelligent Tutoring System (ITS) so that it can guide a learner during problem-solving learning activities. However, for many ill-defined domains, the domain knowledge is hard to define explicitly. In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions, and how it can support tutoring services during problem-solving exercises. This article describes an extension of this approach to extract a problem space that is richer and more adapted for supporting tutoring services. We combined sequential pattern mining with (1) dimensional pattern mining (2) time intervals, (3) the automatic clustering of valued actions and (4) closed sequences mining. Some tutoring services have been implemented and an experiment has been conducted in a tutoring system.

Children's Interactions with Inspectable and Negotiated Learner Models

Abstract: The Learner Model of an Intelligent Tutoring System (ITS) may be made visible (opened) to its users. An Open Learner Model (OLM) may also become a learning resource in its own right, independently of an ITS. OLMs offer potential for learner reflection and support to metacognitive skills such as self-assessment, in addition to improving learner model accuracy. This paper describes an evaluation of an inspectable and a negotiated OLM (one that can be jointly maintained through student-system discussion) in terms of facilitating self-assessment accuracy and modification of model contents. Both inspectable and negotiated models offered significant support to users in increasing the accuracy of self-assessments, and reducing the number and magnitude of discrepancies between system and user beliefs about the user's knowledge. Negotiation of the model demonstrated further significant improvements.

An agent based intelligent tutoring system for parameter passing in java programming

Abstract: We have developed an agent based intelligent tutoring system for the parameter passing mechanisms in computer science (2), an introductory Java programming language, in Al-Azhar University in Gaza. The agent based intelligent tutoring system helps students better understand parameter passing mechanisms in Java using problem based technique. In this paper, we will describe the architectural design and features of the agent based intelligent tutoring system. An initial evaluation of effectiveness of the system was carried out and the result was found to be positive. The evaluation confirmed the established hypothesis that using the intelligent tutoring system would result in an improvement in the learning of the students [5]-[9].

Mining and visualizing visited trails in web-based educational systems

Abstract: A data mining and visualization tool for the discovery of student trails in web-based educational systems is presented and described. The tool uses graphs to visualize results, allowing non-expert users, such as course instructors, to interpret its output. Several experiments have been conducted, using real data collected from a web-based intelligent tutoring system. The results of the data mining algorithm can be adjusted by tuning its parameters or filtering to concentrate on specific trails, or to focus only on the most significant paths.

An Intelligent Tutoring System for the Accounting Cycle: Enhancing Textbook Homework with Artificial Intelligence

Abstract: This paper describes the implementation of artificial intelligence (AI) in electronic tutoring systems, and demonstrates an AI-based tutor that has been recently developed in accounting to provide instruction about the accounting cycle. Empirical findings indicate that use of this tutor in a 50-minute homework session contributed to an improvement in test performance of approximately 27 percentage points; in comparison, students using their textbook and course notes to complete the same homework improved their test performance by about 8 percentage points. Future research opportunities are discussed.

The Politeness Effect in an Intelligent Foreign Language Tutoring System

Abstract: A previous study showed that pedagogical agents that offer feedback with appropriate politeness strategies can help students learn better [21]. This work studied the Politeness Effect in a foreign language intelligent tutoring system, and provided further evidence that tutorial feedback with socially intelligent strategies can influence motivation and learning outcomes.

Comparing Learners' Affect While Using an Intelligent Tutoring System and a Simulation Problem Solving Game

Abstract: We compare the affect associated with an intelligent tutoring environment, Aplusix, and a simulations problem solving game, The Incredible Machine, to determine whether students experience significantly better affect in an educational game than in an ITS. We find that affect was, on the whole, better in Aplusix than it was in The Incredible Machine. Students experienced significantly less boredom and frustration and more flow while using Aplusix. This implies that, while aspects unique to games (e.g. fantasy and competition) may make games more fun, the interactivity and challenge common to both games and ITSs may play a larger role in making both types of systems affectively positive learning environments.

Research Methods Tutor: evaluation of a dialogue-based tutoring system in the classroom.

Abstract: Research Methods Tutor (RMT) is a dialogue-based intelligent tutoring system for use in conjunction with undergraduate psychology research methods courses. RMT includes five topics that correspond to the curriculum of introductory research methods courses: ethics, variables, reliability, validity, and experimental design. We evaluated the effectiveness of the RMT system in the classroom using a nonequivalent control group design. Students in three classes (n = 83) used RMT, and students in two classes (n = 53) did not use RMT. Results indicated that the use of RMT yielded strong learning gains of 0.75 standard deviations above classroom instruction alone. Further, the dialogue-based tutoring condition of the system resulted in higher gains than did the textbook-style condition (CAI version) of the system. Future directions for RMT include the addition of new topics and tutoring elements.

Annotating Students’ Understanding of Science Concepts

Abstract: This paper summarizes the annotation of fine-grained entailment relationships in the context of student answers to science assessment questions. We annotated a corpus of 15,357 answer pairs with 145,911 fine-grained entailment relationships. We provide the rationale for such fine-grained analysis and discuss its perceived benefits to an Intelligent Tutoring System. The corpus also has potential applications in other areas, such as question answering and multi-document summarization. Annotators achieved 86.2% inter-annotator agreement (Kappa=0.728, corresponding to substantial agreement) annotating the fine-grained facets of reference answers with regard to understanding expressed in student answers and labeling from one of five possible detailed relationship categories. The corpus described in this paper, which is the only one providing such detailed entailment annotations, is available as a public resource for the research community. The corpus is expected to enable application development, not only for intelligent tutoring systems, but also for general textual entailment applications, that is currently not practical.

Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test

Abstract: It has been reported in previous work that students' online tutoring data collected from intelligent tutoring systems can be used to build models to predict actual state test scores. In this paper, we replicated a previous study to model students' math proficiency by taking into consideration students' response data during the tutoring session and their help-seeking behavior. To extend our previous work, we propose a new method of using students test scores from multiple years (referred to as cross-year data) for determining whether a student model is as good as the standardized test to which it is compared at estimating student math proficiency. We show that our model can do as well as a standardized test. We show that what we assess has prediction ability two years later. We stress that the contribution of the paper is the methodology of using student cross-year state test score to evaluate a student model against a standardized test.

Personalized Intelligent M-learning System for Supporting Effective English Learning

Abstract: To provide an effective and flexible learning environment for english learning, this study adopts the advantages of the mobile learning to present a personalized intelligent m-learning system (PIMS) which can appropriately recommend english news articles to learners based on the learners' reading abilities evaluated by the proposed fuzzy item response theory (FIRT) for non-native english speakers. In addition, to promote an individual's ability to read news articles in english, the new or unfamiliar vocabularies of the individual learner can also be automatically discovered and retrieved from the reading english news articles by the PIMS system according to the english vocabulary ability of the individual learner for enhancing vocabulary learning.

The Composition Effect: Conjuntive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS.

Abstract: Multi skill scenarios are common place in real world problems and Intelligent Tutoring System questions alike, however, system designers have often relied on ad-hoc methods for modeling the composition of multiple skills. There are two common approaches to determining the probability of correct for a multi skill question: a conjunctive approach, which assumes that all skills must be known or a compensatory approach which assumes that the strength of one skill can compensate for the weakness of another skill. We compare the conjunctive model to a learned compositional function and find that the learned function quite nearly converges to the conjunctive function. We can confidently report that system designers can implement the AND gate to represent the composition function quite accurately. Cognitive modelers may be interested in the small compensatory effect that is present. We use a static Bayesian network to model the two hypotheses and use the expectationmaximization algorithm to learn the parameters of the models.

A natural language intelligent tutoring system for training pathologists: implementation and evaluation

Abstract: Introduction We developed and evaluated a Natural Language Interface (NLI) for an Intelligent Tutoring System (ITS) in Diagnostic Pathology. The system teaches residents to examine pathologic slides and write accurate pathology reports while providing immediate feedback on errors they make in their slide review and diagnostic reports. Residents can ask for help at any point in the case, and will receive context-specific feedback. Research questions We evaluated (1) the performance of our natural language system, (2) the effect of the system on learning (3) the effect of feedback timing on learning gains and (4) the effect of ReportTutor on performance to self-assessment correlations. Methods The study uses a crossover 2 × 2 factorial design. We recruited 20 subjects from 4 academic programs. Subjects were randomly assigned to one of the four conditions—two conditions for the immediate interface, and two for the delayed interface. An expert dermatopathologist created a reference standard and 2 board certified AP/CP pathology fellows manually coded the residents’ assessment reports. Subjects were given the opportunity to self grade their performance and we used a survey to determine student response to both interfaces. Results Our results show a highly significant improvement in report writing after one tutoring session with 4–fold increase in the learning gains with both interfaces but no effect of feedback timing on performance gains. Residents who used the immediate feedback interface first experienced a feature learning gain that is correlated with the number of cases they viewed. There was no correlation between performance and self-assessment in either condition.

T-MAESTRO and its authoring tool: using adaptation to integrate entertainment into personalized t-learning

Abstract: Interactive Digital TV opens new learning possibilities where new forms of education are needed. On the one hand, the combination of education and entertainment is essential to boost the participation of viewers in TV learning (t-learning), overcoming their typical passiveness. On the other hand, researchers broadly agree that in order to prevent the learner from abandoning the learning experience, it is necessary to take into account his/her particular needs and preferences by means of a personalized experience. Bearing this in mind, this paper introduces a new approach to the conception of personalized t-learning: edutainment and entercation experiences. These experiences combine TV programs and learning contents in a personalized way, with the aim of using the playful nature of TV to make learning more attractive and to engage TV viewers in learning. This paper brings together our work in constructing edutainment/entercation experiences by relating TV and learning contents. Taking personalization one step further, we propose the adaptation of learning contents by defining A-SCORM (Adaptive-SCORM), an extension of the ADL SCORM standard. Over and above the adaptive add-ons, this paper focuses on two fundamental entities for the proposal: (1) an Intelligent Tutoring System, called T-MAESTRO, which constructs the t-learning experiences by applying semantic knowledge about the t-learners; and (2) the authoring tool which allow teachers to create adaptive courses with a minimal technical background.

Self Versus Teacher Judgments of Learner Emotions During a Tutoring Session with AutoTutor

Abstract: The relationship between emotions and learning was investigated by tracking the affective states that college students experiencedwhile interacting with AutoTutor, an intelligent tutoring system with conversational dialogue. An emotionally responsive tutor would presumably facilitate learning, but this would only occur if learner emotions can be accurately identified. After a learning session with AutoTutor, the affective states of the learner were classified by the learner and two accomplished teachers. The classification of the teachers was not very reliable and did not match the learners self reports. This result suggests that accomplished teachers may be limited in detecting the affective states of learners. This paper discusses the implications of our findings for theories of expert tutoring and for alternate methodologies for establishing convergent validity of affect measurement.

A kst-based system for student tutoring

Abstract: A novel assessment procedure based on knowledge space theory (KST) is presented along with a complete implementation of an intelligent tutoring system (ITS) that has been used to test our theoretical findings. The key idea is that correct assessment of the student knowledge is strictly related to the structure of the domain ontology. Suitable relationships between the concepts must be present to allow the creation of a reverse path from the "knowledge state" representing the student goal to the one that contains her actual knowledge about this topic. Knowledge space theory is a very good framework to guide the process of building the ontology used by the artificial tutor. The system we present uses a conversational agent to assess the student knowledge through a natural language question/answer procedure. The system exploits a Cyc-based common sense ontology about the specific domain of interest to select the concepts needed to explain unknown topics emerging from the dialogue. Besides, the latent semantic analysis (LSA) technique is used to determine the correctness of the student sentences in order to establish which concepts she knows. As a result, the system supplies learning material arranged as a path between the unknown topics resulting from the student assessment. The learning path is presented to the student by a user-friendly graphical interface, which allows to access documents browsing a visual map. The procedure is explained in detail along with the rest of the system, and the assessment validation results are presented.

Subliminal priming enhances learning in a distant virtual 3D Intelligent Tutoring System

Abstract: In this paper we discuss the use of subliminal priming in a novel way in the context of a 3D virtual tutoring system. Subliminal priming is a technique used to project information to a learner outside of his perceptual field. Subliminal projections have been used in various fields but never in the domain of 3D Intelligent Tutoring Systems. We also monitored the physiological reactions of the user while they learn. We will present the virtual environment and the subliminal module used. The results of this study show that learners do feel motivated to learn in a 3D environment and enjoy the experience even if they hardly have any video gaming experience at all. Furthermore, subliminal priming, even though not consciously perceived by a learner, seems to elicit strong physiological reactions as well as positively impacts performance.

Learning Linked Lists: Experiments with the iList System

Abstract: This paper presents the first experiments with an Intelligent Tutoring System in the domain of linked lists, a fundamental topic in Computer Science. The system has been deployed in an introductory college-level Computer Science class, and engendered significant learning gains. A constraint-based approach has been adopted in the design and implementation of the system. We describe the system architecture, its current functionalities, and the future directions of its development.

Coaching intercultural communication in a serious game

Abstract: We consider the use of virtual humans and an intelligent tutoring system (ITS) for the teaching of cultural social conventions. Learning occurs in a serious game that requires the learner to establish trust and reach agreements with virtual characters of a different culture. Our tutoring system provides culturally focused learning support during and after the meetings with these virtual characters. In a study intended to determine the effectiveness of the ITS, we found that guidance provided during meetings seemed to improve learners’ understandings of culturally-related “phases” in meetings (e.g., when to talk about business) as well as greater success in an unsupported posttest meeting, but with no overall increase in cultural understanding when compared with learning in passive and unguided conditions.

HERA: Learner Tracking in a Virtual Environment

Abstract: The main goals of using simulations and Virtual Environments for Training/Learning (VET/L) are to avoid risks and unwanted consequences, to reduce training costs, and to promote trial and error as an effective strategy for learning. Such environments should enable monitoring of the plans followed by learners, the errors they commit and the resulting risks, and show learners the impact of their decisions during the training process. In this paper, we present a research work that aims to develop a learner tracking system in a virtual environment equipped with an Intelligent Tutoring System (ITS), that we have called HERA (Helpful agent for safEty leaRning in virtuAl environment). This system is composed of different models representing the data sources, and other modules corresponding to the main processes charged with exchanging, analyzing, transforming, registering, and interpreting data. Thanks to the collaboration between these models and modules, HERA is able to determine learners' current tasks, the errors and risks produced, and to help them during and after learning. Our system allows learners to perform their tasks in front of a screen, and to see 1) the impact of their decisions by modifying the Virtual Environment, and 2) a textual feedback of their activities (tasks and errors) in a list form.

Integrating Case-Based Reasoning and Meta-Learning for a Self-Improving Intelligent Tutoring System

Abstract: A framework integrating case-based reasoning (CBR) and meta-learning is proposed in this paper as the underlying methodology enabling self-improving intelligent tutoring systems (ITSs). Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module - cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. To minimize canceling out effects due to the multiple strategies used for meta-learning - for example, the learning result of one strategy undoes or reduces the impact of the learning result of another strategy, a principled design that is both cautious and prioritized is put in place. An ITS application, called Intelligent Learning Material Delivery Agent (ILMDA), has been implemented, powered by this framework, on introductory computer science topics, and deployed at the Computer Science and Engineering Department of the University of Nebraska. Studies show the feasibility of such a framework and impact analyses are reported on pedagogical strategies and outcomes.

Individualizing Tutoring with Learning Style Based Feedback

Abstract: To approximate more closely effective human tutors, intelligent tutoring systems should adapt not only to a student's knowledge but also her learning style. We introduce a pedagogical framework that incorporates the Felder-Silverman learning style model and validated instrument for assessing individual learning style. The framework provides a feedback infrastructure based on the learning style model dimensions (such as visual, verbal, intuitive, sensor, etc.). It has been implemented as part of the DesignFirst-ITS, helping novices learn how to design a class in UML from a problem description. The system has been evaluated with high-school students and results show that learning style based feedback helps students realize higher learning gains.