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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]

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]

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]

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]

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]