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Artificial intelligence (AI) has seen rapid advancements, establishing itself as a robust tool for fostering accessible teaching and learning environments. Nevertheless, the efficacy of these intelligent learning settings in bolstering the engagement and learning outcomes for students with hearing impairments warrants further investigation. This research leveraged Design-Based Research (DBR) to iteratively refine intelligent learning environments, while also adopting the Community of Inquiry (COI) and Technology Acceptance Model (TAM) to assess classroom presence and perception among students with hearing impairments. The results revealed notable enhancements in teaching presence, cognitive presence, social presence, and overall perception within intelligent learning contexts for hearing-impaired students. Additionally, their understanding of abstract and ill-structured knowledge significantly improved. Such findings offer valuable insights for the design of inclusive teaching and learning environments in special education.

This work seeks to contribute to the development of intelligent environments by presenting an approach oriented to the identification of On-Task and Off-Task behaviors in educational settings. This is accomplished by monitoring and analyzing the user-object interactions that users manifest while performing academic activities with a tangible-intangible hybrid system in a university intelligent environment configuration. With the proposal of a framework and the Orange Data Mining tool and the Neural Network, Random Forest, Naive Bayes, and Tree classification models, training and testing was carried out with the user-object interaction records of the 13 students (11 for training and two for testing) to identify representative sequences of behavior from user-object interaction records. The two models that had the best results, despite the small number of data, were the Neural Network and Naive Bayes. Although a more significant amount of data is necessary to perform a classification adequately, the process allowed exemplifying this process so that it can later be fully incorporated into an intelligent educational system. [ABSTRACT FROM AUTHOR]

Student feedback analysis is time-consuming and laborious work if it is handled manually. This study explores the use of a new deep learning-based method to design a more accurate automated system for analysing students' feedback (called DTLP: deep learning and teaching process). The DTLP employs convolutional neural networks (CNNs), bidirectional LSTM (BiLSTM), and attention mechanism. To the best of our knowledge, a deep learning-based method using a unified feature set, which is representative of word embedding, sentiment knowledge, sentiment shifter rules, linguistic and statistical knowledge, has not been thoroughly studied with regard to sentiment analysis of student feedback. Furthermore, DTLP uses multiple strategies to overcome the following drawbacks: contextual polarity; sentence types; words with similar semantic context but opposite sentiment polarity; word coverage limit of an individual lexicon; and word sense variations. To evaluate the DTLP, we conducted an experiment on a large volume of students' feedback. The results showed (i) DTLP outperforms the existing systems in the field, (ii) DTLP that learns from this unified feature set can acquire significantly higher performance than one that learns from a feature subset, (iii) the ensemble of sentiment shifter rules, word embedding, statistical, linguistic, and sentiment knowledge allows DTLP to obtain significant performance, and (iv) an attention mechanism into CNN-BiLSTM improves the performance of DTLP. In addition, the deployed method looks for potential causes behind student feedback. [ABSTRACT FROM AUTHOR]

Adaptive learning has garnered researchers' interest. The main issue within this field is how to select appropriate learning objects (LOs) based on learners' requirements and context, and how to combine the selected LOs to form what is known as an adaptive learning path. Heuristic and metaheuristic approaches have achieved significant progress on personalized and adaptive recommendations, but the operators of some heuristic algorithms are often fixed which decreases the algorithms' extendibility. This paper reviews existing works and proposes an innovative approach. We model the proposed approach as a constraints satisfaction problem, and an improved genetic algorithm named adaptive genetic algorithm is proposed to solve it. The proposed solution does not only reduce the search space size and increase search efficiency but also it is more explicit in finding the best composition of LOs for a specific learner. As a result, the best personalized adaptive learning resources combination will be found in lesser time. [ABSTRACT FROM AUTHOR]

This work describes a learning tool named ARGeoITS that combines augmented reality with an intelligent tutoring system to support geometry learning. The work depicts a study developed in Mexico to measure the impact on the learning and motivation of students using two different learning systems: an intelligent tutoring system with augmented reality (ARGeoITS) and a system with only augmented reality (ARGeo). To study the effect of this type of technology (ARGeoITS, ARGeo) and time of assessment (pre-, post-) on learning gains and motivation, we applied a 2 × 2 factorial design to 106 middle school students. Both pretest and post-test questionnaires were applied to each group to determine the students' learning gains, as was an IMMS motivational survey to evaluate the students' motivation. The results show that: (1) students who used the intelligent tutoring system ARGeoITS scored higher in learning gain (7.47) compared with those who used ARGeo (6.83); and (2) both the ARGeoITS and ARGeo learning tools have a positive impact on students' motivation. The research findings imply that intelligent tutoring systems that integrate augmented reality can be exploited as an effective learning environment to help middle–high school students learn difficult topics such as geometry. [ABSTRACT FROM AUTHOR]

This article presents a survival analysis-based model to estimate the duration of the emotion of confusion in students during learning. Academic confusion can have both positive and negative effects, and its persistence can lead to negative emotions. The model considers crucial factors such as personality traits and students' prior knowledge, which have been shown to significantly influence the duration of confusion. To investigate this relationship, data were collected from seventh-grade students who used an intelligent tutoring system to solve algebra problems. The results of the analysis of data from 25 students revealed statistically significant differences in the duration of confusion based on different personality traits and prior algebra knowledge. Additionally, a model of intervention for intelligent learning environments was proposed when students are confused, based on the developed model. This module determines the optimal time to intervene and provide individualized assistance tailored to the student's knowledge for the specific problem. The study contributes to understanding the dynamics of academic confusion and highlights the importance of considering emotions and their duration, as well as personality traits and prior knowledge, when designing appropriate interventions in intelligent learning environments. Identifying the opportune moment to intervene when a student is confused is essential to promote a more effective learning process, allowing educators to adopt personalized approaches to meet individual students' needs and facilitate their learning journey. [ABSTRACT FROM AUTHOR]

This work describes a learning tool named ARGeoITS that combines augmented reality with an intelligent tutoring system to support geometry learning. The work depicts a study developed in Mexico to measure the impact on the learning and motivation of students using two different learning systems: an intelligent tutoring system with augmented reality (ARGeoITS) and a system with only augmented reality (ARGeo). To study the effect of this type of technology (ARGeoITS, ARGeo) and time of assessment (pre-, post-) on learning gains and motivation, we applied a 2 × 2 factorial design to 106 middle school students. Both pretest and post-test questionnaires were applied to each group to determine the students’ learning gains, as was an IMMS motivational survey to evaluate the students’ motivation. The results show that: (1) students who used the intelligent tutoring system ARGeoITS scored higher in learning gain (7.47) compared with those who used ARGeo (6.83); and (2) both the ARGeoITS and ARGeo learning tools have a positive impact on students’ motivation. The research findings imply that intelligent tutoring systems that integrate augmented reality can be exploited as an effective learning environment to help middle–high school students learn difficult topics such as geometry.

The use of recommendation systems has been extensively applied to different fields from the suggestion of multimedia content to other areas such as education. Thereof, the integration of affect-related information becomes a key factor to enhance the user experience and, when it comes to learning, it can be translated into the maximization of knowledge acquisition. The existence of affect-enhanced datasets is imperative when training a recommender learning system. This article introduces a new dataset gathered under realistic conditions, which includes affect-related information of 33 learners from different knowledge background interacting with an e-learning platform. Matrix factorization techniques were evaluated in order to prove the wealthiness of the dataset and to demonstrate that it could potentially replace the adaptation mechanism of the introduced platform and maximizing the learner's knowledge acquisition. [ABSTRACT FROM AUTHOR]

In this paper, we present the development of an opinion‐mining module. The development of the module consisted of creating an emotion tagged dataset of opinions; implementing an opinion mining module that processes sentences about computer programming, predicting or recognizing their polarity (positive/negative) and their type of emotion (frustrated, bored, excited, engagement, and neutral); and integrating the previous module in an intelligent learning environment. We evaluated the corpus, the accuracy of text polarity, and emotion recognition. The results with respect to polarity are promising (88.26%), however, the results in the detection of emotions are still low (60.0%). The reasons which likely explain these outcomes include a relatively small (7,777 records) and unbalanced corpus. [ABSTRACT FROM AUTHOR]

This article examines the educational efficacy of a learning environment in which children diagnosed with Autism Spectrum Conditions (ASC) engage in social interactions with an artificially intelligent (AI) virtual agent and where a human practitioner acts in support of the interactions. A multi-site intervention study in schools across the UK was conducted with 29 children with ASC and learning difficulties, aged 4—14 years old. For reasons related to data completeness and amount of exposure to the AI environment, data for 15 children was included in the analysis. The analysis revealed a significant increase in the proportion of social responses made by ASC children to human practitioners. The number of initiations made to human practitioners and to the virtual agent by the ASC children also increased numerically over the course of the sessions. However, due to large individual differences within the ASC group, this did not reach significance. Although no evidence of transfer to the real-world post-test was shown, anecdotal evidence of classroom transfer was reported. The work presented in this article offers an important contribution to the growing body of research in the context of AI technology design and use for autism intervention in real school contexts. Specifically, the work highlights key methodological challenges and opportunities in this area by leveraging interdisciplinary insights in a way that (i) bridges between educational interventions and intelligent technology design practices, (ii) considers the design of technology as well as the design of its use (context and procedures) on par with one another, and (iii) includes design contributions from different stakeholders, including children with and without ASC diagnosis, educational practitioners, and researchers. [ABSTRACT FROM AUTHOR]

Sophisticated educational technologies are evolving rapidly, and online courses are becoming more easily available, generating interest in innovating lightweight data-driven adaptive approaches that foster responsive teaching and improving the overall learning experience. However, in most existing adaptive educational systems, the black-box modeling of learner and instructional models based on the views of a few designers or experts tended to drive the adaptation of learning content. However, different sources of uncertainty could affect these views, including how accurately the proposed adaptive educational methods actually assess student responses and the corresponding uncertainties associated with how students receive and comprehend the resulting instruction. E-learning environments contain high levels of linguistic uncertainties, whereby students can interpret and act on the same terms, words, or methods (e.g., course difficulty, length of study time, or preferred learning style) in various ways according to varying levels of motivation, pre-knowledge, cognition, and future plans. Thus, one adaptive instructional model does not fit the needs of all students. Basing the instruction model on determining learners' interactions within the learning environment in interpretable and easily read white-box models is crucial for adapting the model to students' needs and understanding how learning is realized. This paper presents a new zSlices-based type-2 fuzzy-logic-based system that can learn students' preferred knowledge delivery needs based on their characteristics and current levels of knowledge to generate an adaptive learning environment. We have evaluated the proposed system's efficiency through various large-scale, real-world experiments involving 1871 students from King Abdulaziz University. These experiments demonstrate the proposed zSlices type-2 fuzzy-logic-based system's capability for handling linguistic uncertainties to produce better performance, particularly in terms of enhanced student performance and improved success rates compared with interval type-2 fuzzy logic, type-1 fuzzy systems, adaptive, instructor-led systems, and non-adaptive systems. [ABSTRACT FROM AUTHOR]

It is well known that learners using intelligent learning environments make different use of the feedback provided by the intelligent learning environment and exhibit different patterns of behaviour. Traditional approaches to measuring such behaviour have focused on observational methods, think-aloud protocols, ratings and log data. More recently, the field of educational neuroscience has placed greater emphasis on real-time measures using eye tracking, electroencephalogram and functional magnetic resonance imaging. Our work fits into the latter vein. Drawing on a literature review from cognitive psychology, neuroscience and education, we describe our Learner Processing of Feedback in Intelligent Learning Environments model of how learners process feedback. We also present findings from a pilot study as a preliminary test of the model. Seventeen learners participated in an experiment using the intelligent learning environment known as Crystal Island. A range of data was collected, including a pre-test measuring prior knowledge, think-alouds, log data, video recordings, biometrics and post-task questionnaires. We discuss these findings and steps forward to further validate the model using physiological measures. [ABSTRACT FROM AUTHOR]

Cultural awareness, when applied to Intelligent Learning Environments (ILEs), contours the overall appearance, behaviour, and content used in these systems through the use of culturally-relevant student data and information. In most cases, these adaptations are system-initiated with little to no consideration given to student-initiated control over the extent of cultural-awareness being used. This paper examines some of the issues relevant to these challenges through the development of the ICON (Instructional Cultural cONtextualisation) system. The paper explores computational approaches for modelling the diversity of students within subcultures, and the necessary semantic formalisms for representing and reasoning about cultural backgrounds at an appropriate level of granularity for ILEs. The paper investigates how student-initiated control of dynamic cultural adaptation of educational content can be achieved in ILEs, and examines the effects of cultural variations of language formality and contextualisation on student preferences for different types of educational content. Evaluations revealed preliminary insight into quantifiable thresholds at which student perception for specific types of culturally-contextualised content vary. The findings further support the notion put forth in the paper that student-initiated control of cultural contextualisation should be featured in ILEs aiming to cater for diverse groups of students. [ABSTRACT FROM AUTHOR]

In e-learning environments, tutors perform the main function of tutoring. They follow up learners and answer their assistance requests which require different skills. These requests may not belong to the tutors' skills and competencies, so collaboration among other tutors is expected. In fact, this collaboration can improve tutors' skills and provide learners with effective monitoring. It can be permanent and adaptive, according to learners' needs and requests. Grouping tutors with different skills is the aim of this research. In this paper, a new technique for grouping tutors is presented. The proposed technique is called K-complementarity. It is based on the complementarity of roles that are assigned to tutors. K-complementarity is based on tutor model and aims at obtaining K groups of tutors who have the most of the roles and skills. The proposed technique has been used by a Computer-Supported Collaborative Tutoring (CSCTT) system. This system had been tested in an Algerian University. The obtained results showed that the groups' members are heterogeneous and the groups are homogeneous. Furthermore, each group had more than 80% of the roles by combining those of its members. So, these results can be considered as acceptable and very encouraging. [ABSTRACT FROM AUTHOR]

Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners' affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners' affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy. [ABSTRACT FROM AUTHOR]

Modern e-learning systems use new information and communications technologies (ICT) for improvement of education process. Elaborated Intelligent Teaching System (ISN) ensures optimum learning (enabling individualization of learning process) by the use of artificial intelligence tools). Creation of appropriate learning environment considering pedagogical theories of learning process is important (ensuring its creative and cognitive nature) Specialized e-learning systems (enabling individualization of learning process and considering kind of disability) are especially useful in education of disabled persons [ABSTRACT FROM AUTHOR]

The article focuses on the application of computer traces of interactions in teaching and training activities that involve digital environment. It states that the intelligent learning environment allows learners to interact with others and to have access to all types of teaching elements. It mentions that some digital environments use computer traces of interactions to learners as a way to facilitate their appropriation of intelligent learning environments. It cites a form of digital environment that enables the learner to gain access on visualization of interaction traces and allows them to browse in this information. It recommends that the choice of digital devices that provide a de facto visualization of the interaction traces could allow learners to control their learning processes.

Today's technology-enhanced learning practices cater to students and teachers who use many different learning tools and environments and are used to a paradigm of interaction derived from open, ubiquitous, and socially oriented services. In this context, a crucial issue for education systems in general, and for Intelligent Learning Environments (ILEs) in particular, is related to the ability of leveraging these new paradigms for creating, maintaining and sharing the knowledge that these systems embed. This will enable ILEs to benefit from shared information from disparate systems, which is related to learning content and student activities, so that the overall complexity of system development and maintenance would be reduced while at the same time improving the capability of personalization, context-awareness, and interaction. In this article, we investigate how the Social Semantic Web can be leveraged for enabling and easing this process. We first analyze each module of a typical ILE, showing how it can benefit from the Social Semantic Web paradigm and then proceed to investigate how this new paradigm can be leveraged for increasing interactivity level of ILEs. [ABSTRACT FROM AUTHOR]

Abstract: Collaborative student modeling in adaptive learning environments allows the learners to inspect and modify their own student models. It is often considered as a collaboration between students and the system to promote learners’ reflection and to collaboratively assess the course. When adaptive learning environments are used in the classroom, teachers act as a guide through the learning process. Thus, they need to monitor students’ interactions in order to understand and evaluate their activities. Although, the knowledge gained through this monitorization can be extremely useful to student modeling, collaboration between teachers and the system to achieve this goal has not been considered in the literature. In this paper we present a framework to support teachers in this task. In order to prove the usefulness of this framework we have implemented and evaluated it in an adaptive web-based educational system called PDinamet. [Copyright &y& Elsevier]

This paper introduces AMPLIA, an intelligent learning environment employed as a resource in medical students' training. The development of AMPLIA raised several research topics, due to the convergence of Artificial Intelligence (AI) and Learning Environments. The core topics are: probabilistic diagnostic learning in the medical area, application of teaching strategies based on Pedagogical Negotiation (PN), construction of cognitive student models with probabilistic beliefs, and application of interoperability methods for pedagogical agents, and tutoring systems integration. An important aspect of AMPLIA is the utilization of PN as the main form of interaction. The impact of this approach on the system dynamics and on the student's learning is presented in detail. Considering the importance of cooperative work during the learning process, we describe how AMPLIA is used to enable cooperation. The description is based on experiments carried out with AMPLIA and its users. The main results of these experiments are reported as well. [ABSTRACT FROM AUTHOR]

In this paper, we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning. This work contributes to user modeling and intelligent interfaces research by extending existing research on eye-tracking in HCI to on-line capturing of high-level user mental states for real-time interaction tailoring. We first describe the empirical work we did to understand the user meta-cognitive behaviors to be modeled. We then illustrate the probabilistic user model we designed to capture these behaviors with the help of on-line information on user attention patterns derived from eye-tracking data. Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence. Finally, we discuss work we have done on using pupil dilation information, also gathered through eye-tracking data, to further improve model accuracy. [Copyright &y& Elsevier]

Abstract: In this paper, the implementation of a neural network-based fuzzy modeling approach to assess aspects of students’ learning style in the discovery learning environment “Vectors in Physics and Mathematics” is presented. Fuzzy logic is used to provide a linguistic description of students’ behavior and learning characteristics, as they have been elicited from teachers, and to handle the inherent uncertainty associated with teachers’ subjective assessments. Neural networks are used to add learning and generalization abilities to the fuzzy model by encoding teachers’ experience through supervised neural-network learning. The neural network-based fuzzy diagnostic model is a general diagnostic model which is implemented in an Intelligent Learning Environment by eliciting teachers’ expertise regarding students’ characteristics based on real students’ observation and on data being collected from students’ interaction. The model has been successfully implemented, trained and tested in the learning environment “Vectors in Physics and Mathematics” by using the recommendations of a group of five experienced teachers. The performance of our model in real classroom conditions has been evaluated during an experiment with an experienced Physics teacher and 49 students of secondary school attending Physics lessons. [Copyright &y& Elsevier]

In spite of an increasing development of virtual and distant applications which use the advantages of multimedia and the Internet for distance education, learning by means of such tutorial tools would be more effective if they were specifically adapted to each user needs. This paper proposes a knowledge representation model which judiciously serves the remediation process of an intelligent learning environment in interaction with students during virtual learning activities. By means of experimental results obtained thanks to practical tests, we show that our knowledge representation model facilitates the planning of a tailored sequence of feedbacks that significantly help the learner. [ABSTRACT FROM AUTHOR]

For the past few years, the storytelling research domain has raised the interest of several researchers from different areas (such as interactive drama, computer games, intelligent learning environment etc.), mainly because stories are an important vehicle to structure the human knowledge and past experience, and even to help people to behave in a social context. In this paper, we present a storytelling approach, which is based on the premise that story and narrative may emerge from characters’ interactions during the creation activity, but such characters may either act autonomously or be controlled by children. Since we position our research in an educational context, we argue that in such creation activities children’s thoughts and needs must be respected and contribute directly to the achievement of the story. Therefore, children should be provided with some support and guidance directives to help them to better understand and reflect on the role-taking situations they experience during such activity. To prove the validity of our approach we developed a generic architecture—Support And Guidance Architecture (SAGA). SAGA aims not only to provide support and guidance to children during the story telling activities, by combining the roles of a dynamic script writer and a drama manager, but also to provide such services to already existing story creation applications. To illustrate this, we have integrated SAGA with Teatrix, a collaborative virtual environment for story creation, and an evaluation study was conducted in order to find the impact of SAGA in children’s storytelling activities. [ABSTRACT FROM AUTHOR]

Researchers in the field of AI and Law have developed a number of computational models of the arguments that skilled attorneys make based on past cases. However, these models have not accounted for the ways that attorneys use middle-level normative background knowledge (1) to organize multi-case arguments, (2) to reason about the significance of differences between cases, and (3) to assess the relevance of precedent cases to a given problem situation. We present a novel model, that accounts for these argumentation phenomena. An evaluation study showed that arguments about the significance of distinctions based on this model help predict the outcome of cases in the area of trade secrets law, confirming the quality of these arguments. The model forms the basis of an intelligent learning environment called CATO, which was designed to help beginning law students acquire basic argumentation skills. CATO uses the model for a number of purposes, including the dynamic generation of argumentation examples. In a second evaluation study, carried out in the context of an actual legal writing course, we compared instruction with CATO against the best traditional legal writing instruction. The results indicate that CATO''s example-based instructional approach is effective in teaching basic argumentation skills. However, a more “integrated” approach appears to be needed if students are to achieve better transfer of these skills to more complex contexts. CATO''s argumentation model and instructional environment are a contribution to the research fields of AI and Law, Case-Based Reasoning, and AI and Education. [Copyright &y& Elsevier]

Framed by the existing theoretical and empirical research on cognitive and intelligent tutoring systems (ITSs), this commentary explores two areas not directly or extensively addressed by Akhras and Self (this issue). The first area focuses on the lack of conceptual clarity of the proposed constructivist stance and its related constructs (e.g., affordances, situations). Specifically, it is argued that a clear conceptualization of the novel constructivist stance needs to be delineated by the authors before an evaluation of their ambitious proposal to model situations computationally in intelligent learning environments (ILEs) can be achieved. The second area of exploration deals with the similarities between the proposed stance and existing approaches documented in the cognitive, educational computing, and AI in education literature. I believe that the authors are at a crossroads, and that their article presents an initial conceptualization of an important issue related to a constructivist-based approach to the computational modeling of situations in ILEs. However, conceptual clarity is definitively required in order for their approach to be adequately evaluated and used to inform the design of ILEs. As such, I invite the authors to re-conceptualize their framework by addressing how their constructivist stance can be used to address a particular research agenda on the use of computers as metacognitive tools to enhance learning. [ABSTRACT FROM AUTHOR]

• Creation of two dataset for emotions and sentiments in text. • Recognitions of learning-centered emotions in text. • Comparison among machine & deep learning methods against an evolutionary approach. • Integration of best classification model to an intelligent learning environment. This paper presents a comparison among several sentiment analysis classifiers using three different techniques – machine learning, deep learning, and an evolutionary approach called EvoMSA – for the classification of educational opinions in an Intelligent Learning Environment called ILE-Java. To make this comparison, we develop two corpora of expressions into the programming languages domain, which reflect the emotional state of students regarding teachers, exams, homework, and academic projects, among others. A corpus called sentiTEXT has polarity (positive and negative) labels, while a corpus called eduSERE has positive and negative learning-centered emotions (engaged, excited, bored, and frustrated) labels. From the experiments carried out with the three techniques, we conclude that the evolutionary algorithm (EvoMSA) generated the best results with an accuracy of 93% for the corpus sentiTEXT, and 84% for the corpus eduSERE. [ABSTRACT FROM AUTHOR]