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Showing papers on "Intelligent tutoring system published in 2014"


Journal ArticleDOI
TL;DR: Why ASSISTments has been successful and what lessons were learned are shared and goals for the future will be presented.
Abstract: The ASSISTments project is an ecosystem of a few hundred teachers, a platform, and researchers working together. Development professionals help train teachers and get teachers to participate in studies. The platform and these teachers help researchers (sometimes explicitly and sometimes implicitly) simply by using content the teacher selects. The platform, hosted by Worcester Polytechnic Institute, allows teachers to write individual ASSISTments (composed of questions with answers and associated hints, solutions, web-based videos, etc.) or to use pre-built ASSISTments, bundle them together in a problem set, and assign these to students. The system gives immediate feedback to students while they are working and provides student-level data to teachers on any assignment. The word “ASSISTments” blends tutoring “assistance” with “assessment” reporting to teachers and students. While originally focused on mathematics, the platform now has content from many other subjects (e.g., science, English, Statistics, etc.). Due to the large library of mathematics content, however, it is mostly used by math teachers. Over 50,000 students used ASSISTments last school year (2013–4) and this number has been doubling each year for the last 8 years. The platform allows any user, mostly researchers, to create randomized controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies. The data collected by the system has also been used in a few dozen peer-reviewed data mining publications. This paper will not seek to review these publications, but instead we will share why ASSISTments has been successful and what lessons were learned along the way. The first lesson learned was to build a platform for learning sciences, not a product that focused on a math topic. That is, ASSISTments is a tool, not a curriculum. A second lesson learned is expressed by the mantra “Put the teacher in charge, not the computer.” This second lesson is about building a flexible system that allows teachers to use the tool in concert with the classroom routine. Once teachers are using the tool they are more likely to want to participate in research studies. These lessons were born from the design decisions about what the platform supports and does not support. In conclusion, goals for the future will be presented.

325 citations


Journal ArticleDOI
TL;DR: Gender is an important factor for feedback efficiency: Male students achieve significantly lower knowledge gains than female students under all tutoring feedback conditions (particularly, under feedback strategies starting with a conceptual hint), and results indicate that students skip further attempts more frequently after conceptual than after procedural feedback messages.
Abstract: Personalized tutoring feedback is a powerful method that expert human tutors apply when helping students to optimize their learning. Thus, research on tutoring feedback strategies tailoring feedback according to important factors of the learning process has been recognized as a promising issue in the field of computer-based adaptive educational technologies. Our paper seeks to contribute to this area of research by addressing the following aspects: First, to investigate how students' gender, prior knowledge, and motivational characteristics relate to learning outcomes (knowledge gain and changes in motivation). Second, to investigate the impact of these student characteristics on how tutoring feedback strategies varying in content (procedural vs. conceptual) and specificity (concise hints vs. elaborated explanations) of tutoring feedback messages affect students' learning and motivation. Third, to explore the influence of the feedback parameters and student characteristics on students' immediate post-feedback behaviour (skipping vs. trying to accomplish a task, and failing vs. succeeding in providing a correct answer). To address these issues, detailed log-file analyses of an experimental study have been conducted. In this study, 124 sixth and seventh graders have been exposed to various tutoring feedback strategies while working on multi-trial error correction tasks in the domain of fraction arithmetic. The web-based intelligent learning environment ActiveMath was used to present the fraction tasks and trace students' progress and activities. The results reveal that gender is an important factor for feedback efficiency: Male students achieve significantly lower knowledge gains than female students under all tutoring feedback conditions (particularly, under feedback strategies starting with a conceptual hint). Moreover, perceived competence declines from pre- to post-test significantly more for boys than for girls. Yet, the decline in perceived competence is not accompanied by a decline in intrinsic motivation, which, instead, increases significantly from pre- to post-test. With regard to the post-feedback behaviour, the results indicate that students skip further attempts more frequently after conceptual than after procedural feedback messages.

156 citations


Book ChapterDOI
05 Jun 2014
TL;DR: This paper investigated the usefulness of eye tracking data for predicting emotions relevant to learning, specifically boredom and curiosity, and used a variety of machine learning and feature selection techniques to predict students' self-reported emotions from gaze data features.
Abstract: In this paper we investigate the usefulness of eye tracking data for predicting emotions relevant to learning, specifically boredom and curiosity. The data was collected during a study with MetaTutor, an intelligent tutoring system ITS designed to promote the use of self-regulated learning strategies. We used a variety of machine learning and feature selection techniques to predict students' self-reported emotions from gaze data features. We examined the optimal amount of interaction time needed to make predictions, as well as which features are most predictive of each emotion. The findings provide insight into how to detect when students disengage from MetaTutor.

119 citations


Journal ArticleDOI
TL;DR: The results of this study showed that students perceived the W-Pal system as informative, valuable, and enjoyable, and results also highlighted specific ways that these aspects of the system could be further enhanced.

109 citations


Proceedings Article
04 Jul 2014
TL;DR: FAST (Feature Aware Student knowledge Tracing), an efficient, novel method that allows integrating general features into Knowledge Tracing, is presented and it is reported that using features can improve up to 25% in classification performance of the task of predicting student performance.
Abstract: Knowledge Tracing is the de-facto standard for inferring student knowledge from performance data. Unfortunately, it does not allow modeling the feature-rich data that is now possible to collect in modern digital learning environments. Because of this, many ad hoc Knowledge Tracing variants have been proposed to model a specific feature of interest. For example, variants have studied the effect of students’ individual characteristics, the effect of help in a tutor, and subskills. These ad hoc models are successful for their own specific purpose, but are specified to only model a single specific feature. We present FAST (Feature Aware Student knowledge Tracing), an efficient, novel method that allows integrating general features into Knowledge Tracing. We demonstrate FAST’s flexibility with three examples of feature sets that are relevant to a wide audience. We use features in FAST to model (i) multiple subskill tracing, (ii) a temporal Item Response Model implementation, and (iii) expert knowledge. We present empirical results using data collected from an Intelligent Tutoring System. We report that using features can improve up to 25% in classification performance of the task of predicting student performance. Moreover, for fitting and inferencing, FAST can be 300 times faster than models created in BNT-SM, a toolkit that facilitates the creation of ad hoc Knowledge Tracing variants.

65 citations


Journal ArticleDOI
TL;DR: Students' help-seeking behaviors in an intelligent tutoring system were analyzed and it was investigated whether the use of these strategies could be predicted by achievement goal scores.
Abstract: Help seeking behavior in an intelligent tutoring system was analyzed to identify help seeking strategies, and it was investigated whether the use of these strategies could be predicted by achievement goal scores. Discrete Markov Models and a k-means clustering algorithm were used to identify strategies, and logistic regression analyses (n?=?45) were used to analyze the relation between achievement goals and strategy use. Five strategies were identified, three of which were predicted by achievement goal scores. These strategies were labeled Little Help, Click Through Help, Direct Solution, Step By Step, and Quick Solution. The Click Through Help strategy was predicted by mastery avoidance goals, the Direct Solution strategy was negatively predicted by mastery avoidance goals and positively predicted by performance avoidance goals, and the Quick Solution strategy was negatively predicted by performance approach goals. Students' help-seeking behaviors in an intelligent tutoring system were analyzed.Five help-seeking strategies were identified.The use of three help-seeking strategies could be predicted by achievement goals.

64 citations


Journal ArticleDOI
TL;DR: MetaTutor, an intelligent, multi-agent tutoring system designed to scaffold cognitive and metacognitive self-regulated learning (SRL) processes—interacts with learner’s prior domain knowledge to affect their note-taking activities and subsequent learning outcomes.
Abstract: Hypermedia learning environments (HLE) unevenly present new challenges and opportunities to learning processes and outcomes depending on learner characteristics and instructional supports. In this experimental study, we examined how one such HLE—MetaTutor, an intelligent, multi-agent tutoring system designed to scaffold cognitive and metacognitive self-regulated learning (SRL) processes—interacts with learner’s prior domain knowledge to affect their note-taking activities and subsequent learning outcomes. Sixty (N = 60) college students studied with MetaTutor for 120 min and took notes on hypermedia content of the human circulatory system. Log-files and screen recordings of learner-system interactions were used to analyze notes for several quantitative and qualitative variables. Results show that most note-taking was a verbatim copy of instructional content, which negatively related to the post-test measure of learning. There was an interaction between prior knowledge and pedagogical agent scaffolding, such that low prior knowledge students took a greater quantity of notes compared to their high prior knowledge counterparts, but only in the absence of MetaTutor SRL scaffolding; when agent SRL scaffolding was present, the note-taking activities of low prior knowledge students were statistically equivalent to the number of notes taken by their high prior knowledge counterparts. Theoretical and instructional design implications are discussed.

58 citations


Book ChapterDOI
05 Jun 2014
TL;DR: This work aims at increasing the representational power of the student model by employing dynamic Bayesian networks that are able to represent such skill topologies, and constrain the parameter space to ensure model interpretability.
Abstract: Modeling and predicting student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for student modeling is Bayesian Knowledge Tracing BKT. BKT models, however, lack the ability to describe the hierarchy and relationships between the different skills of a learning domain. In this work, we therefore aim at increasing the representational power of the student model by employing dynamic Bayesian networks that are able to represent such skill topologies. To ensure model interpretability, we constrain the parameter space. We evaluate the performance of our models on five large-scale data sets of different learning domains such as mathematics, spelling learning and physics, and demonstrate that our approach outperforms BKT in prediction accuracy on unseen data across all learning domains.

56 citations


Journal ArticleDOI
TL;DR: In this paper, a large scale randomized controlled trial was conducted to study the efficacy of a web-based intelligent tutoring system for the structure strategy designed to improve content area reading comprehension.
Abstract: This article reports on a large scale randomized controlled trial to study the efficacy of a web-based intelligent tutoring system for the structure strategy designed to improve content area reading comprehension. The research was conducted with 128 fifth-grade classrooms within 12 school districts in rural and suburban settings. Classrooms within each school were randomly assigned to intervention or control groups. The intervention group used the intelligent web-based tutoring system for the structure strategy (ITSS) for 30 to 45 min each week as a partial substitute for the language arts curriculum for the entire school year. The structure strategy teaches students how to read and comprehend expository texts by identifying the text structure and creating strategic mental representations of the text. The web-based tutoring system delivered the structure strategy training with modeling, practice tasks, assessment, and feedback. The control classrooms used the school's language arts curriculum for ...

51 citations


Book ChapterDOI
05 Jun 2014
TL;DR: In this paper, the authors compare collaborative and individual methods while receiving instruction on either procedural or conceptual knowledge and find that collaborative groups had the same learning gains as the individual groups in both the procedural and conceptual learning conditions but were able to do so with fewer problems.
Abstract: Collaborative learning has been shown to be beneficial for older students, but there has not been much research to show if these results transfer to elementary school students. In addition, collaborative and individual modes of instruction may be better for acquiring different types of knowledge. Collaborative Intelligent Tutoring Systems (ITS) provide a platform that may be able to provide both the cognitive and collaborative support that students need. This paper presents a study comparing collaborative and individual methods while receiving instruction on either procedural or conceptual knowledge. The collaborative groups had the same learning gains as the individual groups in both the procedural and conceptual learning conditions but were able to do so with fewer problems. This work indicates that by embedding collaboration scripts in ITSs, collaborative learning can be an effective instructional method even with young children.

50 citations


Journal ArticleDOI
TL;DR: It is found that interleaved practice leads to better learning outcomes than blocked practice on a number of measures, and it is suggested that reactivation, rather than abstraction, is the main mechanism to account for the advantage of interleaves practice.
Abstract: Providing learners with multiple representations of learning content has been shown to enhance learning outcomes. When multiple representations are presented across consecutive problems, we have to decide in what sequence to present them. Prior research has demonstrated that interleaving tasks types (as opposed to blocking them) can foster learning. Do the same advantages apply to interleaving representations? We addressed this question using a variety of research methods. First, we conducted a classroom experiment with an intelligent tutoring system for fractions. We compared four practice schedules of multiple graphical representations: blocked, fully interleaved, moderately interleaved, and increasingly interleaved. Based on data from 230 4th and 5th-grade students, we found that interleaved practice leads to better learning outcomes than blocked practice on a number of measures. Second, we conducted a think-aloud study to gain insights into the learning mechanisms underlying the advantage of interleaved practice. Results show that students make connections between representations only when explicitly prompted to do so (and not spontaneously). This finding suggests that reactivation, rather than abstraction, is the main mechanism to account for the advantage of interleaved practice. Third, we used methods derived from Bayesian knowledge tracing to analyze tutor log data from the classroom experiment. Modeling latent measures of students’ learning rates, we find higher learning rates for interleaved practice than for blocked practice. This finding extends prior research on practice schedules, which shows that interleaved practice (compared to blocked practice) impairs students’ problem-solving performance during the practice phase when using raw performance measures such as error rates. Our findings have implications for the design of multi-representational learning materials and for research on adaptive practice schedules in intelligent tutoring systems.

Journal ArticleDOI
TL;DR: General principles are suggested—which collectively represent a proposed methodology—for the design of intelligent tutoring systems intended for cross-cultural transfer of curriculum and instructional methods for effective mathematics teachers.
Abstract: Effective mathematics teachers have a large body of professional knowledge, which is largely undocumented and shared by teachers working in a given country’s education system. The volume and cultural nature of this knowledge make it particularly challenging to share curricula and instructional methods between countries. Thus, approaches based on knowledge engineering—designing a software system by interviewing human experts to extract their knowledge and heuristics—are particularly promising for cross-cultural curriculum implementations. Reasoning Mind’s Genie 2 system demonstrates the viability of such an approach, bringing elements of Russian mathematics education (known for its effectiveness) to the United States. Genie 2 has been adopted on a large scale, with around 67,000 United States students participating in the 2012–2013 school year. Previously published work (some of it in peer reviewed articles and some in non-peer-reviewed independent evaluations) has associated Genie 2 with high student and teacher acceptance, increases in test scores relative to “business as usual” conditions, high time on task, and a positive affective profile. Here, we describe for the first time the design, function, and use of the Genie 2 system. Based on this work, we suggest general principles—which collectively represent a proposed methodology—for the design of intelligent tutoring systems intended for cross-cultural transfer of curriculum and instructional methods.

Journal ArticleDOI
01 Jun 2014
TL;DR: Some concepts and examples for implementing those method from other papers that can build fundamental component of Intelligent Tutoring System are given.
Abstract: Intelligent Tutoring System is a tutor behaviour system which can be used as an alternative goal for interactive e-learning and distant learning. This system can provide an adaptive system to support student’s learning and retention process based on their characteristic and needed. There are development method such as bayesian network, and neural network that can build fundamental component of Intelligent Tutoring System. This paper will give some concepts and examples for implementing those method from other papers. Index Terms—intelligent tutoring system, artificial intelligent, neural network, bayesian network, ontology

Book ChapterDOI
07 Jul 2014
TL;DR: A study that compares a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adaptively decides how much assistance the student needs.
Abstract: Learning from worked examples has been shown to be superior to unsupported problem solving when first learning in a new domain. Several studies have found that learning from examples results in faster learning in comparison to tutored problem solving in Intelligent Tutoring Systems. We present a study that compares a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adaptively decides how much assistance the student needs. The adaptive strategy determines the type of task (a worked example, a faded example or a problem to be solved) based on how much assistance the student received in the previous problem. The results show that students in the adaptive condition learnt significantly more than their peers who were presented with a fixed sequence of worked examples and problems.

Journal ArticleDOI
TL;DR: The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.
Abstract: Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain knowledge in order to provide feedback to learners adaptively to their needs. This approach implies two general drawbacks: the formalisation of a domain-specific model usually requires a huge effort, and in some domains it is not possible at all. In this paper, we propose feedback provision strategies in absence of a formalised domain model, motivated by example-based learning approaches. We demonstrate the feasibility and effectiveness of these strategies in several studies with experts and students. We discuss how, in a set of solutions, appropriate examples can be automatically identified and assigned to given student solutions via machine learning techniques in conjunction with an underlying dissimilarity metric. The plausibility of such an automatic selection is evaluated in an expert survey, while possible choices for domain-agnostic dissimilarity measures are tested in the context of real solution sets of Java programs. The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.

Journal ArticleDOI
TL;DR: An observational study shows the ITS's accuracy at emulating expert human supervision, and a randomized experiment reveals that the ITS significantly improves students' learning in arithmetical problem solving.
Abstract: This paper presents an intelligent tutoring system (ITS) for the learning of arithmetical problem solving. This is based on an analysis of a) the cognitive processes that take place during problem solving; and b) the usual tasks performed by a human when supervising a student in a one-to-one tutoring situation. The ITS is able to identify the solving strategy that the student is following and offer adaptive feedback that takes into account both the problem's constraints and the decisions previously made by the user. An observational study shows the ITS's accuracy at emulating expert human supervision, and a randomized experiment reveals that the ITS significantly improves students' learning in arithmetical problem solving.

Journal ArticleDOI
TL;DR: The design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis is focused on.
Abstract: We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the student's behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the student's knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.

Journal ArticleDOI
TL;DR: A prototype mobile application has been developed for multiple language learning that incorporates intelligence in its modeling and diagnostic components and the construction of student models which promote the misconception diagnosis is presented.
Abstract: This paper proposes a student-oriented approach tailored to effective collaboration be- tween students using mobile phones for language learning within the life cycle of an intelligent tutoring system. For this reason, in this research, a prototype mobile application has been devel- oped for multiple language learning that incorporates intelligence in its modeling and diagnostic components. One of the primary aims of this research is the construction of student models which promote the misconception diagnosis. Furthermore, they are the key for collaboration, given that students can cooperate with their peers, discuss complex problems from various perspectives and use knowledge to answer questions and/or to solve problems. Summarizing, in this paper, a mobile tutoring framework, built up in the context of student collaboration, is presented. Collaborative student groups are created with respect to the corresponding user models. Finally, the prototype was evaluated and the results confirmed the usefulness of collaborative learning.

Journal ArticleDOI
TL;DR: In this paper, an intelligent tutoring system to improve reading literacy skills called TuinLEC is described. Butte et al. presented the results of its application to a group of sixth grade students and showed that the experimental group significantly outperformed the control group.
Abstract: This study describes an intelligent tutoring system to improve reading literacy skills called TuinLEC and it presents the results of its application to a group of sixth grade students. TuinLEC adopts the reading literacy theoretical framework of PISA (Program for International Students Assessment, OECD, 2009). TuinLEC includes eight lessons distributed in two phases, one for modeling and guided practice, and the second for independent practice. TuinLEC interacts with every student and it provides help and feedback for the task in a game-like environment. Half of the students were taught with TuinLEC, whereas the other half served as the control group. Children in both groups were paired according to reading comprehension scores. We measured students’ reading literacy skills after intervention, which showed that the experimental group significantly outperformed the control group. Students who were taught with TuinLEC were also given a questionnaire measuring satisfaction, usability, and self-effica...

Book ChapterDOI
05 Jun 2014
TL;DR: An extension to the Cognitive Tutor Authoring Tools to allow for development of collaborative ITSs through multiple synchronized tutor engines is discussed, a step forward in blending computer-supported collaborative learning and ITS technologies in an effort to combine their strengths.
Abstract: Authoring tools have been shown to decrease the amount of time and resources needed for the development of Intelligent Tutoring Systems (ITSs). Although collaborative learning has been shown to be beneficial to learning, most of the current authoring tools do not support the development of collaborative ITSs. In this paper, we discuss an extension to the Cognitive Tutor Authoring Tools to allow for development of collaborative ITSs through multiple synchronized tutor engines. Using this tool, an author can combine collaboration with the type of problem solving support typically offered by an ITS. Different phases of collaboration scripts can be tied to particular problem states in a flexible, problem-specific way. We illustrate the tool’s capabilities by presenting examples of collaborative tutors used in recent studies that showed learning gains. The work is a step forward in blending computer-supported collaborative learning and ITS technologies in an effort to combine their strengths.

Book ChapterDOI
07 Jul 2014
TL;DR: This study develops sensor-free affect detection for EcoMUVE, an immersive multi-user virtual environment that teaches middle-school students about casualty in ecosystems and constructed models for five different educationally-relevant affective states, paving the way for the design of adaptive personalization.
Abstract: The application of educational data mining (EDM) techniques to interactive learning software is increasingly being used to broaden the range of constructs typically incorporated in student models, moving from traditional assessment of student knowledge to the assessment of engagement, affect, strategy, and metacognition. Researchers are also broadening the range of environments within which these constructs are assessed. In this study, we develop sensor-free affect detection for EcoMUVE, an immersive multi-user virtual environment that teaches middle-school students about casualty in ecosystems. In this study, models were constructed for five different educationally-relevant affective states (boredom, confusion, delight, engaged concentration, and frustration). Such models allow us to examine the behaviors most closely associated with particular affective states, paving the way for the design of adaptive personalization to improve engagement and learning.

01 Jan 2014
TL;DR: An Intelligent Tutoring Framework highly re-usable and suitable to several knowledge domains, named ABITS, is described, able to support a Web-based Course Delivery Platform with a set of “intelligent” functions providing both student modeling and automatic curriculum generation.
Abstract: The purpose of this paper is to describe an Intelligent Tutoring Framework highly re-usable and suitable to several knowledge domains. In particular the system, named ABITS, has been realized in the context of the InTraSys ESPRIT project. It is able to support a Web-based Course Delivery Platform with a set of “intelligent” functions providing both student modeling and automatic curriculum generation. Such functions found their effectiveness on a set of rules for knowledge indexing based on Metadata and Conceptual Graphs following the IEEE Learning Object Metadata (LOM) standard. Moreover, in order to ensure the maximal flexibility, ABITS is organized as a Multi Agent System (MAS) composed by pools of three different kind of agents (evaluation, pedagogical and affective agents). Each agent is able to solve in autonomous way a specific task and they work together in order to improve the WBT learning effectiveness adapting the didactic materials to user skills and preferences.

Journal ArticleDOI
TL;DR: This article outlines a proposal about how to use eLearning standard and automatic assessment techniques to build an ITS for learning to program and shows the implemented system to test the proposal and how it has been evaluated with students in the authors' university.
Abstract: Computer Programming competence is a good research field in which students of Computer Science can be assisted by an Intelligent Tutoring System (ITS). An ITS can guide the students in their learning process proposing the corresponding learning activities for each particular student. In this article, we will outline a proposal about how to use eLearning standard and automatic assessment techniques to build an ITS for learning to program. In addition, we will show the implemented system to test our proposal and how we have evaluated it with students in our university.© 2012 Wiley Periodicals, Inc. Comput Appl Eng Educ 22:774–787, 2014; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21569

Journal ArticleDOI
TL;DR: The Structured Plug-in Integrated Teaching and Learning Assistance System was developed, which aims to achieve affirmative results from ICT on education and has been used in six classes of 219 fourth-year students in Korean elementary schools.
Abstract: There have been numerous attempts to heighten the effects of education over the course of history. Currently, many relevant studies are being carried out on such issues as utilization of recently developed Information and Communication Technology (ICT) for education, which has become a global topic; efforts are being put into this movement at the national level. However, the systems to assist the efforts are not yet sufficient. Through this research, in this light, the Structured Plug-in Integrated Teaching and Learning Assistance System was developed, which aims to achieve affirmative results from ICT on education. This system can provide the instructor, learner, and their parents with a real-time monitoring system, intelligent tutoring system, collaborative education mechanism, e-Portfolio system, and digital material production method. Further, the system can be realized in the form of a Structured Plug-in. The system has been used in six classes of 219 fourth-year students in Korean elementary schools to analyze the system's effects. In doing so, a questionnaire was developed and carried out on the students after the testing period. According to the results, the overall satisfaction level was 4.067 and the level of satisfaction on the content quality was 3.99, referring to `satisfaction'.

Journal ArticleDOI
TL;DR: The architecture of intelligent tutoring system is based on wordnet based ontology with expert knowledge, which has been used for repository resource indexing, and which is a basic component of domain model.

Proceedings ArticleDOI
TL;DR: An overview of the design of a conversational intelligent tutoring system, called DeepTutor, based on the framework of Learning Progressions, which aims to capture students' successful paths towards mastery.
Abstract: We present an overview of the design of a conversational intelligent tutoring system, called DeepTutor, based on the framework of Learning Progressions. Learning Progressions capture students' successful paths towards mastery. The assumption of the proposed tutor is that by guiding instruction based on Learning Progressions, the system will be more effective (and efficient for that matter).

Proceedings ArticleDOI
01 Jan 2014
TL;DR: The aim of this paper is to investigate the most recent state of art in the development of the tutor model and student model of the intelligent tutoring systems.
Abstract: The intelligent tutoring system (ITS) is an educational software system that provides personalized and adaptive tutoring to students based on their needs, profiles and preferences. The tutor model and student model are two dependent components of any ITS system. The goal of any ITS system is to help the students to achieve maximum learning gain and improve their engagements to the systems by capturing the student's interests through the system's adaptive behavior. In other words an ITS system is always developed with the aim of providing an immediate and efficient solution to student's learning problems. In recent years a lot of work has been devoted to improving student and tutor models in order enhance the teaching and learning activities within the ITS systems. The aim of this paper is to investigate the most recent state of art in the development of these two vital components of the intelligent tutoring systems.

Journal ArticleDOI
TL;DR: This paper highlights the approach taken by the PHP ITS to analyse students’ programs that include a number of program constructs that are used by beginners of web development.
Abstract: Programming is a subject that many beginning students find difficult. The PHP Intelligent Tutoring System (PHP ITS) has been designed with the aim of making it easier for novices to learn the PHP language in order to develop dynamic web pages. Programming requires practice. This makes it necessary to include practical exercises in any ITS that supports students learning to program. The PHP ITS works by providing exercises for students to solve and then providing feedback based on their solutions. The major challenge here is to be able to identify many semantically equivalent solutions to a single exercise. The PHP ITS achieves this by using theories of Artificial Intelligence (AI) including first-order predicate logic and classical and hierarchical planning to model the subject matter taught by the system. This paper highlights the approach taken by the PHP ITS to analyse students’ programs that include a number of program constructs that are used by beginners of web development. The PHP ITS was built using this model and evaluated in a unit at the Queensland University of Technology. The results showed that it was capable of correctly analysing over 96 % of the solutions to exercises supplied by students.

Proceedings ArticleDOI
24 Mar 2014
TL;DR: A notion of "strong" learner interest area preferences is defined and it is found that honoring such preferences has a small negative association with performance, and several explanations of these findings are considered.
Abstract: Learners often think math is unrelated to their own interests. Instructional software has the potential to provide personalized instruction that responds to individuals' interests. Carnegie Learning's MATHia™ software for middle school mathematics asks learners to specify domains of their interest (e.g., sports & fitness, arts & music), as well as names of friends/classmates, and uses this information to both choose and personalize word problems for individual learners. Our analysis of MATHia's relatively coarse-grained personalization contrasts with more finegrained analysis in previous research on word problems in the Cognitive Tutor (e.g., finding effects on performance in parts of problems that depend on more difficult skills), and we explore associations of aggregate preference "honoring" with learner performance. To do so, we define a notion of "strong" learner interest area preferences and find that honoring such preferences has a small negative association with performance. However, learners that both merely express preferences (either interest area preferences or setting names of friends/classmates), and those that express strong preferences, tend to perform in ways that are associated with better learning compared to learners that do not express such preferences. We consider several explanations of these findings and suggest important topics for future research.

Book ChapterDOI
05 Jun 2014
TL;DR: Experimental data analysis revealed that learning gains were significantly higher for students randomly assigned to the fully-adaptive intelligent tutor condition compared to the micro- Adaptive-only condition.
Abstract: We present in this paper the findings of a study on the role of macro-adaptation in conversational intelligent tutoring. Macro-adaptivity refers to a system's capability to select appropriate instructional tasks for the learner to work on. Micro-adaptivity refers to a system's capability to adapt its scaffolding while the learner is working on a particular task. We compared an intelligent tutoring system that offers both macro- and micro-adaptivity fully-adaptive with an intelligent tutoring system that offers only micro-adaptivity. Experimental data analysis revealed that learning gains were significantly higher for students randomly assigned to the fully-adaptive intelligent tutor condition compared to the micro-adaptive-only condition.