Adaptive Tutoring System Research Articles

Affective Response Categories-Toward Personalized Reactions in Affect-Adaptive Tutoring Systems.

Affect-adaptive tutoring systems detect the current emotional state of the learner and are capable of adequately responding by adapting the learning experience. Adaptations could be employed to manipulate the emotional state in a direction favorable to the learning process; for example, contextual help can be offered to mitigate frustration, or lesson plans can be accelerated to avoid boredom. Safety-critical situations, in which wrong decisions and behaviors can have fatal consequences, may particularly benefit from affect-adaptive tutoring systems, because accounting for affecting responses during training may help develop coping strategies and improve resilience. Effective adaptation, however, can only be accomplished when knowing which emotions benefit high learning performance in such systems. The results of preliminary studies indicate interindividual differences in the relationship between emotion and performance that require consideration by an affect-adaptive system. To that end, this article introduces the concept of Affective Response Categories (ARCs) that can be used to categorize learners based on their emotion-performance relationship. In an experimental study, N = 50 subjects (33% female, 19–57 years, M = 32.75, SD = 9.8) performed a simulated airspace surveillance task. Emotional valence was detected using facial expression analysis, and pupil diameters were used to indicate emotional arousal. A cluster analysis was performed to group subjects into ARCs based on their individual correlations of valence and performance as well as arousal and performance. Three different clusters were identified, one of which showed no correlations between emotion and performance. The performance of subjects in the other two clusters benefitted from negative arousal and differed only in the valence-performance correlation, which was positive or negative. Based on the identified clusters, the initial ARC model was revised. We then discuss the resulting model, outline future research, and derive implications for the larger context of the field of adaptive tutoring systems. Furthermore, potential benefits of the proposed concept are discussed and ethical issues are identified and addressed.

Linking Dialogue with Student Modelling to Create an Adaptive Tutoring System for Conceptual Physics

Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However, considerably less attention has been paid to using a student model to personalize instruction in tutorial dialogue systems (TDSs)—ITSs that engage students in natural-language, conceptual discussions. This paper describes Rimac, a TDS that tightly couples student modelling with tutorial dialogues about conceptual physics. Rimac is distinct from other TDSs insofar as it dynamically builds a persistent student model that guides reactive and proactive decision making in order to provide adaptive instruction. An initial pilot study set in high school physics classrooms compared a control version of Rimac without a student model with an experimental version that implemented a “poor man’s student model”; that is, the model was initialized using students’ pretest scores but not updated further. Both low and high prior knowledge students showed significant pretest to posttest learning gains. However, high prior knowledge students who used the experimental version of Rimac learned more efficiently than high prior knowledge students who used the control version. Specifically, high prior knowledge students who used the student model driven tutor took less time to complete the intervention but learned a similar amount as students who used the control version. A subsequent study found that both high and low prior knowledge students learned more efficiently from a version of the tutor that dynamically updates its student model during dialogues than from a control version that included the static “poor man’s student model.” We discuss future work needed to improve the performance of Rimac’s student model and to integrate TDSs in the classroom.

Toward a Definition of Learning Experience Design

Various theories and models have implicitly discussed the role of interaction when using learning technologies. Indeed, interaction is described as being important as it relates to technology adoption, cognitive load, and usability. While each of these perspectives describe elements of interaction, they fail to comprehensively detail how educators should design for both usability and learning with an interface. To address this gap, this work-in-progress study seeks to describe the broader interaction when using learning technology, which we define as learning experience design. Using grounded theory and related eye-tracking data, we asked participants to engage in a cognitive think-aloud as they utilized an adaptive tutoring system. When triangulated, the researchers identified the following broad constructs: interaction with the learning environment and interaction with the learning space. The former includes the following codes: customization, expectation of content placement, functionality of component parts, interface terms aligned with existing mental models, and navigation. Alternatively, the interaction with the learning space included the following: engagement with the modality of content, dynamic interaction, perceived value of technology feature to support learning, and scaffolding. Implications for both theory and practice are discussed.

A Novel Teaching Strategy Through Adaptive Learning Activities for Computer Programming

Contribution: This article presents the instruction of computer programming using adaptive learning activities considering students' cognitive skills based on the learning theory of the Revised Bloom Taxonomy (RBT). To achieve this, the system converts students' knowledge level to fuzzy weights, and using rule-based decision making, delivers adequate learning activities regarding their kind and complexity degree. Background: Tutoring through adaptive learning activities can be a powerful tool to support learners in practical courses, like computer programming. However, published results from pertinent literature are not oriented to the adaptivity of the domain knowledge in terms of the volume, kind, and complexity of the learning activities delivered to students. There is scope for a lot of improvement to this direction. Intended Outcomes: Combining learning theories with adaptive tutoring enhances student-centered learning, promotes student engagement, and improves knowledge acquisition. Application Design: An adaptive tutoring system was developed for supporting undergraduate students in the C# programming language course for an academic semester. The system delivers adaptive learning activities to students' cognitive skills using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of a RBT for designing the learning material. Findings: At the end of the academic semester, students' data have been collected and a detailed evaluation was conducted. The results showed that the presented approach outperforms others which lack adaptivity in domain knowledge and learning theories, improving significantly the students' learning outcomes.

Self - Adaptive and Interactive Tutoring System(SA-ITS) for Mathematics Learners

Self - Adaptive and Interactive Tutoring System(SA-ITS) for Mathematics Learners

Methodological apparatus and instruments for personalization in adaptive tutoring systems

Methodological apparatus and instruments for personalization in adaptive tutoring systems

Adaptive tutoring system based on fuzzy logic

In recent years, education method has changed and has become very innovative and modern. In this way, online adaptive learning seems to be a revolutionary competitive method. The advancement of computer and networking technologies is the key to this whole change from the classic education to the modern online adaptive education. The majority of E-learning systems are based on Boolean logic. In fact, the system considers that the learner like or not a course characteristic but the user can prefer gradually this parameter (low, medium, high). To this end, the proposed approach exploits semantic relations between data elements and learners' preferences to determine adapted UI components appropriate to learners' characteristics based on fuzzy logic. The results of evaluation confirm the efficiency of our technique with an average of more than 77% of precision and recall.

Evaluating Human–Automation Etiquette Strategies to Mitigate User Frustration and Improve Learning in Affect-Aware Tutoring

Human–automation etiquette applies human–human etiquette conventions to human–computer interaction (HCI). The research described in this paper investigates how to mitigate user frustration and support student learning through changes in the style in which a computer tutor interacts with a learner. Frustration can significantly impact the quality of learning in tutoring. This study examined an approach to mitigate frustration through the use of different etiquette strategies to change the amount of imposition feedback placed on the learner. An experiment was conducted to explore how varying the interaction style of system feedback impacted aspects of the learning process. System feedback was varied through different etiquette strategies. Participants solved mathematics problems under different frustration conditions with feedback given in different etiquette styles. Changing etiquette strategies from one math problem to the next led to changes in motivation, confidence satisfaction, and performance. The most effective etiquette strategies changed depending on if the user was frustrated or not. This work aims to provide mechanisms to support the promotion of individualized learning in the context of high level math instruction by basing affect-aware adaptive tutoring system design on varying etiquette strategies.

Correction to: A Multimedia Adaptive Tutoring System for Mathematics that Addresses Cognition, Metacognition and Affect

The Publisher regrets a spelling error in the family name of the third author. “Winslow Burelson” should be “Winslow Burleson”.

SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics

BackgroundThis study investigated learning outcomes and user perceptions from interactions with a hybrid intelligent tutoring system created by combining the AutoTutor conversational tutoring system with the Assessment and Learning in Knowledge Spaces (ALEKS) adaptive learning system for mathematics. This hybrid intelligent tutoring system (ITS) uses a service-oriented architecture to combine these two web-based systems. Self-explanation tutoring dialogs were used to talk students through step-by-step worked examples to algebra problems. These worked examples presented an isomorphic problem to the preceding algebra problem that the student could not solve in the adaptive learning system.ResultsDue to crossover issues between conditions, experimental versus control condition assignment did not show significant differences in learning gains. However, strong dose-dependent learning gains were observed that could not be otherwise explained by either initial mastery or time-on-task. User perceptions of the dialog-based tutoring were mixed, and survey results indicate that this may be due to the pacing of dialog-based tutoring using voice, students judging the agents based on their own performance (i.e., the quality of their answers to agent questions), and the students’ expectations about mathematics pedagogy (i.e., expecting to solving problems rather than talking about concepts). Across all users, learning was most strongly influenced by time spent studying, which correlated with students’ self-reported tendencies toward effort avoidance, effective study habits, and beliefs about their ability to improve in mathematics with effort.ConclusionsIntegrating multiple adaptive tutoring systems with complementary strengths shows some potential to improve learning. However, managing learner expectations during transitions between systems remains an open research area. Finally, while personalized adaptation can improve learning efficiency, effort and time-on-task for learning remains a dominant factor that must be considered by interventions.

Adaptive Tutoring System based on Fuzzy Logic

In recent years, education method has changed and has become very innovative and modern. In this way, online adaptive learning seems to be a revolutionary competitive method. The advancement of computer and networking technologies is the key to this whole change from the classic education to the modern online adaptive education. The majority of E-learning systems are based on Boolean logic. In fact, the system considers that the learner like or not a course characteristic but the user can prefer gradually this parameter (low, medium, high). To this end, the proposed approach exploits semantic relations between data elements and learners' preferences to determine adapted UI components appropriate to learners' characteristics based on fuzzy logic. The results of evaluation confirm the efficiency of our technique with an average of more than 77% of precision and recall.

Learning styles based adaptive intelligent tutoring systems: Document analysis of articles published between 2001. and 2016.

Actualizing instructional intercessions to suit learner contrasts has gotten extensive consideration. Among these individual contrast factors, the observational confirmation in regards to the academic benefit of learn- ing styles has been addressed, yet the examination on the issue proceeds. Late improvements in web-based executions have driven researchers to re-examine the learning styles in adaptive tutoring frameworks. Adaptivity in intelligent tutoring systems is strongly influenced by the learning style of a learner. This study involved extensive document analysis of adaptive tutoring systems based on learning styles. Seventy-eight studies in literature from 2001 to 2016 were collected and classified under select parameters such as main focus, purpose, research types, methods, types and levels of participants, field/area of application, learner modelling, data gathering tools used and research findings. The current studies reveal that majority of the studies defined a framework or architecture of adaptive intelligent tutoring system (AITS) while others focused on impact of AITS on learner satisfaction and academic outcomes. Currents trends, gaps in literature and citations were discussed.

DrSust: Adaptive Tutoring System to Increase Students’ Engagement in Programming Learning

DrSust: Adaptive Tutoring System to Increase Students’ Engagement in Programming Learning

Improved modeling of intelligent tutoring systems using ant colony optimization

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is personalized based on learning and solving problem styles. The purposed algorithm, based on ACO, generates the adaptive optimal learning path. The algorithm describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The algorithm parameters are tuned dynamically to conform to the actual pedagogical process. The article includes the results of implementation and experiment represent this algorithm is able to provide its main purpose which is finding optimal learning paths based on learning styles and improved performance of previous adaptive tutoring systems.

Intelligent and Adaptive Tutoring Systems: How to Integrate Learners

This article is a non-expert overview of intelligent technology-based instructional machines, from the first stages of their emergence up to this date. It is attempted to demonstrate their trend of evolution, and how the advances in other related fields such as education, psychology, and computer science affected them and contributed to their progress. It starts by an account of the concept of intelligent technology-based instruction and continues by defining the notion of learner integration in such systems. Furthermore, Intelligent Tutoring Systems (ITSs) are pictured: how they evolved, their architecture, and how they contributed to the field of Adaptive Learning Systems. Finally, a number of adaptive learning platforms which are the state of the art and currently used by large numbers of actual users are introduced and described.

Developing Adaptive and Intelligent Tutoring Systems (AITS)

Several adaptive and intelligent tutoring systems (AITS) have been developed with different variables. These variables were the cognitive traits, cognitive styles, and learning behavior. However, these systems neglect the importance of learner's multiple intelligences, learner's skill level and learner's feedback when implementing personalized mechanisms. In this paper, the authors propose AITS based not only on the learner's multiple intelligences, but also the changing learning performance of the individual learner during the learning process. Therefore, considering learner's skill level and learner's multiple intelligences can promote personalized learning performance. Learner's skill level is obtained from pre-test result analysis, while learner's multiple intelligences are obtained from the analysis of questionnaire. After computing learning success rate of an activity, the system then modifies the difficulty level or the presentation of the corresponding activity to update courseware material sequencing. Learning process in this system is as follows. First, the system determines learning style and characteristics of the learner by an MI-Test and then makes the model. After that it plans a pre-evaluation and then calculates the score. If the learner gets the required score, the activities will be trained. Then the learner will be evaluated by a post-evaluation. Finally the system offers guidance in learning other activities. The proposed system covers all important properties such as hypertext component, adaptive sequencing, problem- solving support, intelligent solution analysis and adaptive presentation while available systems have only some of them. It can significantly improve the learning result. In other words, it helps learners to study in “the best way.”

A Multimedia Adaptive Tutoring System for Mathematics that Addresses Cognition, Metacognition and Affect

This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about students’ mathematics performance. We define progress in three areas: improved student cognition, engagement, and affect, and we attribute this improvement to specific components and interventions that are inherently affective, cognitive, and metacognitive in nature. For instance, improved student cognitive outcomes have been measured with pre-post tests and state standardized tests, and achieved due to personalization of content and math fluency training. Improved student engagement was achieved by supporting students’ metacognition and motivation via affective learning companions and progress reports, measured via records of student gaming of the system. Student affect within the tutor was measured through sensors and student self-reports, and supported through affective learning companions and progress reports. Collectively, these studies elucidate a suite of effective strategies to support advanced personalized learning via an intelligent adaptive tutor that can be tailored to the individual needs, emotions, cognitive states, and metacognitive skills of learners.

Combining Expert Systems and Multiple Intelligences in an Adaptive and Intelligent Tutoring System

This paper describes an adaptive and intelligent tutoring system (AITS) based on multiple intelligences and expert system. Most of adaptive and intelligent tutoring systems based their adaptation to user’s skill level. Other learner features taken into account are background, hyperspace experience, preferences and interests. However, less attention was paid to multiple intelligences and their effects on learning. Moreover, to design AITS which can manage both different disciplinary domains and a guide for the learner is difficult. The specialization of the analysis treatments is responsible for the loss of reusability for the other disciplinary domains. To overcome these limitations, the authors will try to combine the benefits of paradigms (adaptive hypermedia, intelligent tutoring system, multiple intelligences) in order to adapt the course to the needs and intellectual abilities of each learner.

PeRSIVA: An empirical evaluation method of a student model of an intelligent e-learning environment for computer programming

In this paper an evaluation method for assessing the effectiveness, accuracy and validity of a student model was presented. Our method is called PeRSIVA and is a combination of the well-known evaluation method of Kirkpatrick and the layered evaluation framework. These well-known and commonly used evaluation techniques have been selected in order to design an accurate and correct evaluation methodology, since there are no clear guidelines in the literature for the evaluation of the student model of an adaptive tutoring system. Furthermore, PeRSIVA method was used to evaluate the hybrid student model, which combines an overlay model with stereotypes and fuzzy logic techniques, of an e-learning system. Particularly, PeRSIVA assesses the results of student modeling in students' satisfaction, performance, progress, behavior and state, as well as the validity of the conclusions drawn by the student model and the validity of the adaptation decision making. The e-learning system was used by the students of a postgraduate program in the field of informatics in the University of Piraeus and the evaluation results demonstrated learning improvements in students and adaptation success to students' needs.

Using non-verbal cues to (automatically) assess children’s performance difficulties with arithmetic problems

Intelligent tutoring systems often make use of students’ answers to adapt instruction or feedback on a task. In this paper, we explore the alternative possibility of adapting a system based on the perceived affective and cognitive state of a student. A system can potentially better adapt to the needs of each individual student by using non-verbal behavior. We used a new experimental paradigm inspired by ‘brain training’ software to collect primary school children’s answers to easy and difficult arithmetic problems and made audiovisual recordings of their answers. Adult observers rated these films on perceived difficulty level. Results showed that adults were able to correctly interpret children’s perceived level of difficulty, especially if they saw their face (compared to hearing their voice). They paid attention to features such as ‘looking away’, and ‘frowning’. Then we checked whether we could also automatically predict if the posed problem was either easy or difficult based on the first second of their response. This ‘thin-slice analysis’ could correctly predict the difficulty level in 71% of all cases. When trained on sufficiently many recordings, Adaptive Tutoring Systems should be able to detect children’s state and adapt the difficulty level of their learning materials accordingly.