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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.