Showing papers on "Intelligent tutoring system published in 2021"

Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods

Abstract: With the rapid growth of technology, computer learning has become increasingly integrated with artificial intelligence techniques in order to develop more personalized educational systems. ...

The design, implementation and pilot application of an intelligent online proctoring system for online exams

Abstract: Purpose: The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic university online exam and checked the proctoring result. Design/methodology/approach: The IOPS adopts the B/S (Browser/Server) architecture. The server side is implemented with programming language C and Python and stores the identification data of all examinees and their important behavior change status, including facial expression, eye and mouth movement and speech. The browser side collects and analyzes multimodal data of the examinee writing the online test locally and transfers the examinee’s most important behavior status change data to the server. Real-time face recognition and voice detection are implemented with the support of open-source software. Findings: The system was integrated into a Web-based intelligent tutoring system for school mathematics education. As a pilot application, the system was also used for online proctored exam in an undergraduate seminar in Peking University during the epidemic period in 2020. The recorded log data show that all students concentrated themselves on the exam and did not leave the camera and did not speak. Originality/value: During the epidemic period of the novel coronavirus outbreak, almost all educational institutions in the world use online learning as the best way to maintain the teaching and learning schedule for all students. However, current online instruction platforms lack the function to prevent the learners from cheating in online exams and cannot guarantee the integrity and equality for all examinees as in traditional classroom exams. The literature review shows that the online proctoring system should become an important component of online exams to tackle the growing online cheating problem. Although such proctoring systems have been developed and put on the market, the practical usage of such systems in authentic exams and its effect have not been reported. Those systems are heavyweight and commercial product and cannot be freely used in education. The light-weight IOPS developed by the authors could meet the need for online exam as a stable and practical approach and could contribute to the growing online learning and distance learning. © 2021, Emerald Publishing Limited.

An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning

Abstract: The present study aims to examine the pedagogical effectiveness of a Chinese mathematical dialogue-based intelligent tutoring system used for teaching mathematics. The mathematical unit ‘multiplica...

Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving

Abstract: In the present paper, we used supervised machine learning algorithms to predict students' cognitive engagement states from their facial behaviors as 61 students solved a clinical reasoning problem in an intelligent tutoring system. We also examined how high and low performers differed in cognitive engagement levels when performing surface and deep learning behaviors. We found that students' facial behaviors were powerful predictors of their cognitive engagement states. In particular, we found that the SVM (Support Vector Machine) model demonstrated excellent capacity for distinguishing engaged and less engaged states when 17 informative facial features were added into the model. In addition, the results suggested that high performers did not differ significantly in the general level of cognitive engagement with low performers. There was also no difference in cognitive engagement levels between high and low performers when they performed shallow learning behaviors. However, high performers showed a significantly higher level of cognitive engagement than low performers when conducting deep learning behaviors. This study advances our understanding of how students regulate their engagement to succeed in problem-solving. This study also has significant methodological implications for the automated measurement of cognitive engagement.

A differentiated learning environment in domain model for learning disabled learners

Abstract: This paper presents the ontological design and implementation of the differentiated learning environment in the domain model of an intelligent tutoring system for children with specific learning disabilities. It addresses the learners need for differentiated instruction in a preferential learning environment. The proposed model helps to identify the most affected learning domains and related multiple-criteria’s which effects the learners. The learning resources and problems diagnosis questionnaires are organized and used with various learning strategies to create various learning environments such as case-based learning environment, game-based learning environment, practice-based learning environment and visual-based learning environment. Different techniques can define a set of rules to decide the most preferred learning environment. Here, multiple criteria decision analysis approach map the information, learning resources and learning environments to create a differentiated learning environment for the learning disabled. The contribution of proposed model is to reduce the gap between learner and learning habits with special needs. Our model is implemented as domain model of an intelligent tutoring system to develop learner-centric learning environment. In the designed intelligent tutoring system (ITS), the differentiated learning environment domain model is further evaluated and validated by a set of fuzzy rules. The pilot test result shows that proposed model enables an ITS to improve the implementation of appropriate learning strategies with high accuracy and sensitivity for both learning and non-learning-disabled users.

Dynamic Group Formation With Intelligent Tutor Collaborative Learning: A Novel Approach for Next Generation Collaboration

Abstract: Group Formation (GF) strongly influences the collaborative learning process in Computer-Supported Collaborative Learning (CSCL). Various factors affect GF that include personal characteristics, social, cultural, psychological, and cognitive diversity. Although different group formation methods aim to solve the group compatibility problem, an optimal solution for dynamic group formation is still not addressed. In addition, the research lacks to supplement collaborative group formation with a collaborative platform. In this study, the next level of collaboration in CSCL and Intelligent Tutoring System (ITS) platforms is achieved. First, initial groups are formed based on students learning styles, and knowledge level, i.e. for knowledge level, an activity-based dynamic group formation technique is proposed. In this activity, swapping of students takes place on each permutation based on their knowledge level. Second, the formed heterogeneous balanced groups are used to augment the collaborative learning system. For this purpose, a hybrid framework of Intelligent Tutor Collaborative Learning (ITSCL) is used that provides a unique and real-time collaborative learning platform. Third, an experiment is conducted to evaluate the significance of the proposed study. Inferential and descriptive statistics of Paired T-Tests are applied for comprehensive analysis of recorded observations. The statistical results show that the proposed ITSCL framework positively impacts student learning and results in higher learning gains.

Dynamic Key-Value Memory Networks With Rich Features for Knowledge Tracing.

Abstract: Knowledge tracing is an important research topic in student modeling. The aim is to model a student's knowledge state by mining a large number of exercise records. The dynamic key-value memory network (DKVMN) proposed for processing knowledge tracing tasks is considered to be superior to other methods. However, through our research, we have noticed that the DKVMN model ignores both the students' behavior features collected by the intelligent tutoring system (ITS) and their learning abilities, which, together, can be used to help model a student's knowledge state. We believe that a student's learning ability always changes over time. Therefore, this article proposes a new exercise record representation method, which integrates the features of students' behavior with those of the learning ability, thereby improving the performance of knowledge tracing. Our experiments show that the proposed method can improve the prediction results of DKVMN.

A Natural Language Processing-Based Virtual Patient Simulator and Intelligent Tutoring System for the Clinical Diagnostic Process: Simulator Development and Case Study.

Abstract: Background: Shortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators. Objective: The goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator. Methods: We trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case. Results: We developed a VPS called Hepius that allows students to gather clinical information from the patient’s medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P<.001) and mean score for core questions (P<.001) when comparing presimulation and postsimulation performance. Conclusions: By combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.

Exploring Novice Programmers' Hint Requests in an Intelligent Block-Based Coding Environment

Abstract: Block-based programming environments are widely used by novices who are learning computer science. However, even in block-based coding environments that have been carefully developed to serve novices, students frequently struggle and require additional support. A promising avenue to provide this support is the use of intelligent tutoring systems, which offer adaptive hints to assist learners. In order to provide students with the adaptive hints they need, we must investigate their help-seeking behaviors and identify patterns surrounding their need for support. In this experience report, we examine data collected from 174 college students in an introductory engineering course, who used an intelligent block-based coding environment to learn computer science. These students made more than 1,000 hint requests, which we represent in two-dimensional space along axes of elapsed time and code completeness. Analysis revealed five major clusters of hint requests, which we further characterized through qualitative examination of the coding trajectories that preceded each hint request. We also analyzed how students' incoming knowledge and perceived computer skill were related to their help-seeking behaviors. Students with higher incoming knowledge requested hints when their code was more complete than students with lower incoming knowledge. Students with high perceived computer skill asked for hints when their code was less complete than those with low perceived computer skill. The results presented here provide insight into student help-seeking behavior in computer science education, informing CS educators and system designers on how best to develop support strategies.

Intelligent Tutoring Systems: Why Teachers Abandoned a Technology Aimed at Automating Teaching Processes

Abstract: Expectations that technology will improve and streamline education are high However, technology often introduces new problems This study aims to explore the challenges mathematics teachers encounter when they implement a digital mathematics textbook with an integrated intelligent tutoring system A formative intervention was conducted in a two-year project with 16 secondary school teachers The method was based on activity theory and required the teachers to collaborate with researchers in analyzing their work activity when the new teaching tool was introduced In this paper, we show that an intelligent tutoring system created systemic contradictions for the teachers Those contradictions involved predictability, division of labor, individual versus collective learning, accountability, and expectations versus experience The teachers all tried to resolve the contradictions, but eventually felt compelled to abandon the intelligent tutoring system The findings contribute to a better understanding of teachers??? responses to a technology aimed at automating teaching processes

Improving Comprehension: Intelligent Tutoring System Explaining the Domain Rules When Students Break Them

Abstract: Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system.

An intelligent tutoring system architecture based on fuzzy neural network (FNN) for special education of learning disabled learners

Abstract: Several studies have investigated the need for learning difficulties identification specifically Dyslexia, Dysgraphia and Dyscalculia. The identification of these difficulties among children is a multiple screening process under psychologist’s supervision. Learning difficulties identification is a difficult task; it affects the learning process and the academic achievements of a child. The introduction of an Intelligent Tutoring System (ITS) to identify learning problems and teach the learning disabled through ITS is an unexplored domain. An ITS in education is extensively considered for the teaching and learning process as it is an adaptive and learner specific computer system. The capabilities of an ITS in integration with AI methodologies have put together promising results. The ITS framework implemented in this study is developed for learning disabilities identification and we have assessed total 24 participants (with or without Learning Disabilities) for the experiment. This ITS framework design is based on a pretest analysis through initial screening and then system based screening of a child response for Learning Difficulties (LDs) identification. The system based screening is implemented using neural network classifiers to identify learning difficulties. The fuzzy min-max neural network (FMNN) classification is applied to determine learner profile, learning disabled, and present learner-centered content. Fuzzy sets as pattern classes are introduced in supervised learning neural network classification for learner profiling of learning Disabled in an ITS. The results are generated based on the classification applied to the input provided during the pre-test. The results indicate that the integration of fuzzy with the neural network has significantly increased the ITS accuracy.

Meta-Affective Behaviour within an Intelligent Tutoring System for Mathematics

Abstract: Many previous studies have highlighted the influence of learners’ affective states on learning with tutoring systems. However, the associations between learning and learners’ meta-affective capability are still unclear. The goal of this paper is to analyse meta-affective capability and its influence on learning outcomes as well as the dynamics of affect over time. Two criteria, awareness and self-regulation, were employed to define meta-affective capability. An exploratory study (n = 54) was conducted in which students at the secondary level were asked to interact with an intelligent tutoring system for mathematics and to self-report their affect during their interactions with the system. Pre-post learning outcomes were also measured. A post-hoc comparison of learning gains was made between more meta-affectively capable and less meta-affectively capable students. The results provide some empirical evidence to support the hypothesis that having meta-affective capability is positively associated with learning. Students not demonstrating meta-affective capability seemed to transition frequently from boredom to frustration (p = .0284) and from concentration to neutral (p = 0.0017). However, only a small percentage of the sample were classified as having meta-affective capability, indicating that it is important to scaffold students who are not meta-affectively capable.

NLP-based personal learning assistant for school education

Abstract: Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future.

Combined Inner and Outer Loop Feedback in an Intelligent Tutoring System for Statistics in Higher Education.

Abstract: Peer Review The peer review history for this article is available at https://publons.com/publon/10. 1111/jcal.12491. Abstract Intelligent tutoring systems (ITSs) can provide inner loop feedback about steps within tasks, and outer loop feedback about performance on multiple tasks. While research typically addresses these feedback types separately, many ITSs offer them simultaneously. This study evaluates the effects of providing combined inner and outer loop feedback on social sciences students' learning process and performance in a first-year university statistics course. In a 2 x 2 factorial design (elaborate inner loop vs. minimal inner loop and outer loop vs. no outer loop feedback) with 521 participants, the effects of both feedback types and their combination were assessed through multiple linear regression models. Results showed mixed effects, depending on students' prior knowledge and experience, and no overall effects on course performance. Students tended to use outer loop feedback less when also receiving elaborate inner loop feedback. We therefore recommend introducing feedback types one by one and offering them for substantial periods of time.

Development and Promise of a Vocabulary Intelligent Tutoring System for Second-Grade Latinx English Learners.

Abstract: We examine the promise, usability, and feasibility of an intelligent tutoring system (ITS) to improve the vocabulary and language proficiency in science and social studies of Latinx second grade En...

AI and Machine Learning Techniques in the Development of Intelligent Tutoring System: A Review

Abstract: An Intelligent Tutoring System (ITS) is critical in education because it provides one to one personalized teaching assistance to learners as they educate how to solve problems through guidance and prompt feedback. ITS is one application of Artificial Intelligence (AI) in education. It provides a smart learning environment for students without intervention from the teacher. ITS's primary goal is to support and help learners obtain domain-specific intellectual knowledge in a practical and productive manner through the use of different computing technologies. This paper presents a comprehensive survey for previous research on ITS that utilize various techniques of AI and Machine Learning (ML). It gives an overview of ITS, its architecture, and some existing ITS examples. In addition, it highlights and summarizes the current research efforts and obstacles to ITS using AI, as well as some future opportunities. This study shows the importance of AI and ML in ITS development. It is noticed that researchers focus more on Reinforcement Learning (RL), Artificial Neural Networks (ANN), clustering, Bayesian Network (BN) and Fuzzy Logic (FL) approaches.

Teaching and Learning Tools for Introductory Programming in University Courses

Abstract: Difficulties in teaching and learning introductory programming have been studied over the years. The students’ difficulties lead to failure, lack of motivation, and abandonment of courses. The problem is more significant in computer courses, where learning programming is essential. Programming is difficult and requires a lot of work from teachers and students. Programming is a process of transforming a mental plan into a computer program. The main goal of teaching programming is for students to develop their skills to create computer programs that solve real problems. There are several factors that can be at the origin of the problem, such as the abstract concepts that programming implies; the skills needed to solve problems; the mental skills needed to decompose problems; many of the students never had the opportunity to practice computational thinking or programming; students must know the syntax, semantics, and structure of a new unnatural language in a short period of time. In this work, we present a set of strategies, included in an application, with the objective of helping teachers and students. Early identification of potential problems and prompt response is critical to preventing student failure and reducing dropout rates. This work also describes a predictive machine learning (neural network) model of student failure based on the student profile, which is built over the course of programming lessons by continuously monitoring and evaluating student activities.

Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

Abstract: The aim of this study was to predict university students’ learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on posttests of domain knowledge. Our objective was to test whether the prediction could be improved by using attribute selection and classification ensembles. We carried out three experiments by applying six classification algorithms to numerical and discretized preprocessed multimodal data. The results show that the best predictions were produced using ensembles and selecting the best attributes approach with numerical data.

Responsive student model in an intelligent tutoring system and its evaluation

Abstract: In this paper, we introduce a new student responsive model to support students who use an Intelligent Tutoring System (ITS) as an E-Learning tool. We proposed a weighted-based model to estimate and suggest learning materials for students who are pursuing a computer-based course. We have built a brand new ITS called WinITS with our proposed responsive student model and deployed it in Hanoi National University of Education-Vietnam (HNUE) in the second semester of the school year 2019-2020 with a computer science course. To compare the effectiveness of applying ITS to the students, we compare test results and analyze some other aspects related to the course. On the other hand, we conducted a survey between two groups: with and without using WinITS. 63 students are volunteers who participated in the case study. Before learning, 43 students from Group 1 will take a short survey of the Felder-Silverman questionnaire to identify learning styles, after that, they go through all the lessons from the course under the support of WinITS, the lessons will be chosen to satisfy student’s need. On another side, 18 students from Group 2 will make the same test to compare the result to Group 1. In the range of research, we illustrate that our implementation shows some encouraging results such as reducing learning time, improving test score by 1.13 standard deviations, and making the lesson more interesting and flexible. The results have revealed some advantages of studying with computer-added compared to the traditional class in various ways and showed the effectiveness of the proposed model in Intelligent Tutoring Systems.

Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System

Abstract: A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an intelligent tutoring system with computer agents (AutoTutor) designed to improve comprehension skills in adults with low reading literacy. One goal of this study was to classify adults into different clusters based on their behavioral patterns (accuracy and response time to answer questions) while they interacted with AutoTutor to help them improve their reading comprehension skills. A second goal was to investigate whether adults’ behaviors were associated with different reading components. A third goal was to assess improvements in reading comprehension skills, based on psychometric tests, of different clusters of readers. Performance on AutoTutor was collected in a targeted 100-hour hybrid intervention for adult readers (n = 252) that included both human teachers and the AutoTutor system. The adults’ average accuracy and response time in AutoTutor were used to cluster the adults into four categories: higher performers (comparatively fast and accurate), conscientious readers (slow but accurate), under-engaged readers (fast at the expense of somewhat lower accuracy) and struggling readers (slow and inaccurate). Two psychometric tests of comprehension were used to assess comprehension. Gains in comprehension scores were highest for conscientious readers, lowest for struggling readers, with higher performing readers and under-engaged readers in between. The results provide guidance to enhance the adaptivity of AutoTutor.

Using innovative methods to explore the potential of an alerting dashboard for science inquiry

Abstract: Educational technologies, such as teacher dashboards, are being developed to support teachers’ instruction and students’ learning. Specifically, dashboards support teachers in providing the just-in-time instruction needed by students in complex contexts such as science inquiry. In this study, we used the Inq-Blotter teacher-alerting dashboard to investigate whether teacher support elicited by the technology influenced students’ inquiry performance in a science intelligent tutoring system, Inq-ITS. Results indicated that students’ inquiry improved after receiving teachers’ help, elicited by the Inq-Blotter alerts. This inquiry improvement was significantly greater than for matched students who did not receive help from the teacher in response to alerts. Epistemic network analyses were then used to investigate the patterns in the discursive supports provided to students by teachers. These analyses revealed significant differences in the types of support that fostered (versus did not foster) student improvement; differences across teachers were also found. Overall, this study used innovative tools and analyses to understand how teachers use this technological genre of alerting dashboards to dynamically support students in science inquiry.

Automatic tutoring system to support cross-disciplinary training in Big Data

Abstract: During the last decade, Big Data has emerged as a powerful alternative to address latent challenges in scalable data management. The ever-growing amount and rapid evolution of tools, techniques, and technologies associated to Big Data require a broad skill set and deep knowledge of several domains—ranging from engineering to business, including computer science, networking, or analytics among others—which complicate the conception and deployment of academic programs and methodologies able to effectively train students in this discipline. The purpose of this paper is to propose a learning and teaching framework committed to train masters’ students in Big Data by conceiving an intelligent tutoring system aimed to (1) automatically tracking students’ progress, (2) effectively exploiting the diversity of their backgrounds, and (3) assisting the teaching staff on the course operation. Obtained results endorse the feasibility of this proposal and encourage practitioners to use this approach in other domains.

Revealing Data Feature Differences Between System- and Learner-Initiated Self-regulated Learning Processes Within Hypermedia

Abstract: Self-regulated learning (SRL) with advanced learning technologies has shown to significantly augment learners’ performance across contexts. Yet studies find learners lack sufficient SRL skills to successfully implement strategies (e.g., judgments of learning, note taking, self-testing, etc.). Current research does not fully explain how and why this failure of effective strategy deployment occurs. We used principle component analysis (PCA) on process data (i.e., log files) from 190 undergraduates learning with MetaTutor, a hypermedia-based intelligent tutoring system, to explore underlying patterns in the frequency of strategy deployment occurring with and without pedagogical agent scaffolding to better understand any underlying structures of system- and learner-initiated cognitive and metacognitive SRL strategy use. Results showed that the system’s underlying architecture deploys processes corresponding to both the phases of learning and type of effort allocation according to Winne’s (2018) Information Processing Theory of SRL. However, learner-initiated processes for those who received scaffolding only displayed strategy deployment that corresponded to the type of effort allocation required of the processes (i.e., more effortful constructionist processes like note-taking versus short canned responses for judgements of learning). Additionally, results suggest all learners deploy strategies based on the familiarity of processes. Regression models using these principle components outperformed raw frequency models for capturing post-test learning performance across all participants.

An Intelligent Big Data Management System Using Haar Algorithm-Based Nao Agent Multisensory Communication

Abstract: Artificial intelligence (AI) is progressively changing techniques of teaching and learning. In the past, the objective was to provide an intelligent tutoring system without intervention from a human teacher to enhance skills, control, knowledge construction, and intellectual engagement. This paper proposes a definition of AI focusing on enhancing the humanoid agent Nao’s learning capabilities and interactions. The aim is to increase Nao intelligence using big data by activating multisensory perceptions such as visual and auditory stimuli modules and speech-related stimuli, as well as being in various movements. The method is to develop a toolkit by enabling Arabic speech recognition and implementing the Haar algorithm for robust image recognition to improve the capabilities of Nao during interactions with a child in a mixed reality system using big data. The experiment design and testing processes were conducted by implementing an AI principle design, namely, the three-constituent principle. Four experiments were conducted to boost Nao’s intelligence level using 100 children, different environments (class, lab, home, and mixed reality Leap Motion Controller (LMC). An objective function and an operational time cost function are developed to improve Nao’s learning experience in different environments accomplishing the best results in 4.2 seconds for each number recognition. The experiments’ results showed an increase in Nao’s intelligence from 3 to 7 years old compared with a child’s intelligence in learning simple mathematics with the best communication using a kappa ratio value of 90.8%, having a corpus that exceeded 390,000 segments, and scoring 93% of success rate when activating both auditory and vision modules for the agent Nao. The developed toolkit uses Arabic speech recognition and the Haar algorithm in a mixed reality system using big data enabling Nao to achieve a 94% success learning rate at a distance of 0.09 m; when using LMC in mixed reality, the hand sign gestures recorded the highest accuracy of 98.50% using Haar algorithm. The work shows that the current work enabled Nao to gradually achieve a higher learning success rate as the environment changes and multisensory perception increases. This paper also proposes a cutting-edge research work direction for fostering child-robots education in real time.

Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

Abstract: We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.

CompPrehension - Model-Based Intelligent Tutoring System on Comprehension Level

Abstract: Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom’s taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture, easing adding new domains and learning strategies; using formal models and software reasoners to solve the problems and judge the answers; and generating explanatory feedback and follow-up questions to stimulate the learners’ thinking. The architecture and workflow are shown. We demonstrate the process of interacting with the system in the Control Flow Statements domain. The advantages and limits of the developed system are discussed.