Adaptive Intelligent Tutoring System Research Articles

The Effect of Gamified Adaptive Intelligent Tutoring System Artibos on Problem-Solving Skills

This study aims to examine the contribution of ArtiBos, which is designed as a Gamified Adaptive Intelligent Tutoring System for students' problem-solving skills. In the study, first of all, the system’s design features to improve problem-solving skills were examined, and then the effect of the system on problem-solving skills was evaluated. The study was carried out with 12 students studying in the ninth class of a High School in Türkiye and 6 mathematics teachers with different experiences working in the same school. A case study, one of the qualitative research methods were applied in this study through which ArtiBos system logs, student interviews, and teacher interviews were evaluated. Data pertaining to the number of solved problems, the number of problems created, the number of problems solved correctly, the duration of being online in the system, the rate of correct problem-solving, and the average solving time were examined to evaluate system logs. Interview questions have been prepared so that the contribution of system features to problem-solving skills can be evaluated. The data from the interview were analyzed and some codes for problem-solving skills were created. And then, sub-themes were created by combining the codes. The results show that ArtiBos affects students' problem-solving skills positively.

Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Computer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice—one that groups students randomly and one that groups students based on a unidimensional course score. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 2017 and 2019. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability.

From Textbook to Teacher: an Adaptive Intelligent Tutoring System Based on BCI.

In this work, we propose FT3, an adaptive intelligent tutoring system based on Brain Computer Interface(BCI). It can automatically generate different difficulty levels of lecturing video with teachers from textbook adapting to student engagement measured by BCI. Most current studies employ animated images to create pedagogical agents in such adaptive learning environments. However, evidence suggests that human teacher video brings a better learning experience than animated images. We design a virtual teacher generation engine consisting of text-to-speech (TTS) and lip synthesis method, being able to generate high-quality adaptive lecturing clips of talking teachers with accurate lip sync merely based on a textbook and teacher's photo. We propose a BCI to measure engagement, serving as an indicator for adaptively generating appropriate lecturing videos. We conduct a preliminary study to build and evaluate FT3. Results verify that FT3 can generate synced lecturing videos, and provide proper levels of learning content with an accuracy of 73.33%.

Design framework of adaptive intelligent tutoring systems

The aim of this study is to examine adaptation elements and Intelligent Tutoring System (ITS) elements used in Adaptive Intelligent Tutoring Systems (AITSs), using meta-synthesis methods to analyze the results of previous research. Toward this end, articles appearing in the Web of Science, Google Scholar, Eric and Science Direct databases in 2000 and later were identified with the keyphrase “adaptive intelligent tutoring system.” Application of exclusion and inclusion procedures to the articles accessed in the search resulted in the selection of 32 articles, which were analyzed using meta-synthesis methods and then evaluated in the light of prespecified themes and elements used in AITSs were determined. According to results, the systems were designed for a wide range of fields such as Information Technologies, Mathematics, Science, Medicine, and Foreign Language Education. In these systems, content adaptation was generally used, based mostly on such criteria as feedback, student level, student learning and cognitive styles, and student performance. And besides 4 basic ITS modules (knowledge, student, teaching and user interface), some different modules such as guide module, strategy module, personal learning module, knowledge base module, communication module, system administrator module and messaging module were used. Finally, some suggestions were given for such studies in the future.

Implementing an adaptive intelligent tutoring system as an instructional supplement

Evidence is emerging that technology-based curricula and adaptive learning systems can personalize students' learning experiences and facilitate development of mathematical skills. Yet, evidence of efficacy in rigorous studies for these blended instructional models is mixed. These studies highlight challenges implementing the systems in classrooms, which may contribute to a lack of consistently positive effects on student learning. This article extends the literature by closely examining implementation models and dosage levels for a supplemental software, two gaps in existing research. It also investigates adherence to the core components of the software, and extent to which the supplement enabled personalized instruction. The study was conducted in 40 algebra I classes in an urban school district. Sixty-two percent of classes implemented models that integrated instructional modalities. There was mixed adherence to core components of the software in classes that used it. In the vast majority of classes (94%), software did not enable personalized instruction. Software and the existing curricula were largely independent and did not inform each other. Only one class implemented an integrated instructional model, adhered to the core design components of the software, and demonstrated high levels of personalized instruction. Findings identify implementation barriers and offer suggestions for future implementations and studies of technology-enabled personalization.

A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive Intelligent Tutoring System

An ideal face-to-face tutor learner interaction aims to offer learning to the learner in a manner that best suits an individual learner's learning level and learning style. This ability of differentiated instruction has been built in Seis-Tutor Intelligent Tutoring system, developed to offer subject matter knowledge of ‘Seismic Data Interpretation,' a field of geo-physics. The detailed architecture of learner-centric curriculum sequencing module, built to this effect, with its components, sub-components, their interconnected functioning, to generate exclusive learning path, have been described. An algorithm for learner-centric curriculum sequencing, a mathematical model and proposed implementation using a case study has been elaborated.

How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System?

The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students’ (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students’ prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.

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.

Evaluation of Etiquette Strategies to Adapt Feedback In Affect-Aware Tutoring

The purpose of this research is to investigate how to mitigate user frustration and improve task performance in the context of human-computer interaction (HCI). Even though user frustration plays a role in many aspects of HCI and studies have looked into the consequences of frustration in various fields, the ways to mitigate frustration are less deeply examined. Once the system has the ability to understand and include user emotions as factors in HCI, the interaction between the user and the computer system could be adapted if the computers are able to modify its behavior with users in appropriate ways to further joint performance. Specifically, a preliminary study was conducted to explore the task performance, motivation, and confidence implications of changing the interaction between the human and the computer via different etiquette strategies. Participants solved a total of twenty mathematics problems under different frustration condition with feedback given in different styles of etiquette. Changing etiquette strategies in tutoring led to changes in performance, motivation, and confidence. The most effective etiquette strategies changed when users were frustrated. This work provides the foundation for the design of adaptive intelligent tutoring system based on etiquette strategies.

Perceived Task-Difficulty Recognition from Log-file Information for the Use in Adaptive Intelligent Tutoring Systems

Recognising students’ emotion, affect or cognition is a relatively young field and still a challenging task in the area of intelligent tutoring systems. There are several ways to use the output of these recognition tasks within the system. The approach most often mentioned in the literature is using it for giving feedback to the students. The features used for that approach can be high-level features like linguistics features which are words related to emotions or affects, taken e.g. from written or spoken inputs, or low-level features like log-file features which are created from information contained in the log-files. In this work we aim at supporting task sequencing by perceived task-difficulty recognition on low-level features easily extracted from the log-file. We analyse these features by statistical tests showing that there are statistically significant feature combinations and hence the presented features are able to describe students’ perceived task-difficulty in intelligent tutoring systems. Furthermore, we apply different classification methods to the log-file features for perceived task-difficulty recognition and present a kind of higher ensemble method for improving the classification performance on the features extracted from a real data set. The presented approach outperforms classical ensemble methods and is able to improve the classification performance substantially, enabling a perceived task-difficulty recognition satisfactory enough for employing its output for components of a real system like task independent support or task sequencing.

ONLINE FUNCTIONAL LITERACY, INTELLIGENT TUTORING SYSTEMS AND SCIENCE EDUCATION

We live in a time of transition from print reading (off-line) to screen reading (on-line), where the role of the book and other literature is being taken over by different types of electronic devices (computers, tablets, smart phones). In the lives of young people, there is less and less printed media, because it is being pushed out by electronic media. Most written media that is still used is thus bound to the classroom. However, in recent years schools have also become more like e-schools. It is almost impossible to find a school that does not use e-material in its educational process. Research indicates that there are differences in reading comprehension when reading off-line and on-line. In a study in which 78 students from the 8th grade of elementary school participated at the course Technology and science (n=77; 53.2% female), it was shown that in order to overcome this difference, individualised and adaptive Intelligent Tutoring Systems (ITS) can be used. The evaluation of the results also indicates that, for such a form of ITS, there is still plenty of space for optimisation, which is a permanent method of improvement and upgrade in such systems. Key words: reading comprehension, Technology and science, ITS, elementary school.

Orchestrating an Adaptive Intelligent Tutoring System: Towards Integrating the User Profile for Learning Improvement

An intelligent tutoring system aims to provide immediate and customized instruction and feedback to learners. In this context, existing tutoring systems have limitations in the areas of dialogue, feedback and emotion-motivation, which are important elements in the learning process. These aspects are related with the learner abilities, capacities, and motivations. To overcome these limitations, we propose to model the user characteristics that are involved in the learning process and in the human- machine interaction. In this paper we present a proposal to consider an integral user profile in order to gain effectiveness and to achieve more adaptability to the learner.

A Personalized Learning System with Adaptive Content Presentation and Affective Evaluation Facilities

An Intelligent Tutoring System (ITS) should be able to select appropriate chunks of learning materials as well as evaluate learning outcomes while keeping in mind learner’s various meta-cognitive and meta-affective factors. But literature review suggests that such systems are rare as they are complex and time consuming to develop. We have designed an adaptive intelligent tutoring system which is being implemented as a rules-based-expert-system for the dual purpose of - i) adaptive content selection and ii) evaluation of learning gain along with remedial actions. The system is in implementation stage and through this work, we inform in details about the developmental strategies adopted, e.g., use of Java Expert System Shell (JESS) for rules and fact base, Apachetomcat-server for Java implementation. This work also highlights the rule based implementation of domain and affective planner along with details about the rules in textual formats. Our student model is able to recognize learner’s guessing (gaming) behavior, interest, independence, and confidence level. It can also differentiate - a learner’s incorrect answer due to a guess from that due to lack of sufficient domain knowledge. This framework can be used as a guiding principle to build a more robust tutoring system by incorporating other student modeling attributes.

Adaptive intelligent tutoring systems for e-learning systems

An e-learning system is increasingly gaining popularity in the academic community because of several benefits of learning anywhere anyplace and anytime. Most frequently it seems to be used for web-based instruction so that learners can access online courses via internet. One likely reason for the lack of success is that just placing lecture notes on the internet does not train. This situation can be improved through the use of training software such as Intelligent Tutoring Systems (ITS). ITS incorporate built-in expert systems in order to monitor the performance of a learner and to personalize instruction on the basis of adaptation to learners’ learning style, current knowledge level, and appropriate teaching strategies in e-Learning systems. While Adaptive Hypermedia systems (AH) do provide instruction in skills, it is generally less advanced than comparable ITS instruction. E-learning systems are web-based learning so that learners can access online courses via Internet without adaptation based on Learners’ behavior. Therefore, it is a challenge to make e-Learning systems to be more “adaptive”. Both ITS and AH are normally used for computer-based instruction. However, adaptive hypermedia is better suited for the instruction of concepts whereas intelligent tutoring system generally assists in the use of these concepts to solve problems. Therefore, a general instruction system requires both of these instructional approaches in order to provide a full learning environment. In this paper, describes a conceptual for combining ITS and AH into Adaptive Intelligent Tutoring System (AITS) for e-learning systems that allows knowledge to be stored in such a way that is not only independent of the knowledge domain, but also supports the storage of transfer knowledge relationships and prerequisite knowledge relationships. The conceptual results show that this innovative approach is helpful to the learners in improving their learning achievements.

PEGASE, un système tutoriel intelligent générique et adaptatif en environnement virtuel

This work takes place in the framework of the design of a training environment using virtual reality. In this context, we defend the thesis that it is possible to integrate a generic and adaptive intelligent tutoring system (called PEGASE) in a virtual environment, in order to provide pedagogical aid for the learner and pedagogical assistance for the trainer. Our pro- posal is a multi-agent system. It highlights a set of information (actions realized by the learner, domain knowledge...) used by PEGASE to take pedagogical decisions. Our study is focused (1) on knowledge representation for the environment and (2) on an adaptive pedagogical agent suggesting pedagogical assistances.

지능형 교육 시스템을 위한 적응적 지식베이스 객체 모형 개발

Intelligent Tutoring System(ITS), which offers individualized learning environment that consider many learners` variable, is realized by the effective alternative to take the place of domain expert. Accordingly, research on Learning Companion System(LC) is currently noticing. However, to develop LCS which applies effective interaction, it is necessary to combine several LCs, and personalized knowledge base have to be made first. Therefore, in this paper, we propose the `Knowledge Base Object Medel`, which is based on connectionist` in cognition structure, represents learner`s knowledge to self-learnig object, and grows adaptive object by proprietor, verify the validity. This model lays the groundwork for design of personalized knowledge base, offers clue to development of adaptive ITS using knowledge base object.

Implementing Multiple Tutoring Strategies in an Intelligent Tutoring System for Music Learning

Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II. 1.

Implementing multiple tutoring strategies in an intelligent tutoring system for music learning

Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II. 1.