Adaptive Educational Systems Research Articles

INFRASTRUCTURE DESIGN OF THE SERVER PART OF THE ADAPTIVE MOBILE EDUCATIONAL ENVIRONMENT

The articles consider the main approaches for improving the personalization of education in higher education institutions using adaptive educational systems based on information technology. The advantages and efficiency of the adaptive educational system with the use of modern information technologies are emphasized. The general scheme of construction of adaptive mobile educational environment by means of Internet technologies is developed, which has a certain functional structure - a set of functional algorithms that provide user interaction with the system that determines the use and role in the system and internal logic of data blocks. Listening to the functional requirements for creating an adaptive mobile educational environment, 4 main types of users (roles) of systems are identified and previous members of the system are analyzed. The system of distribution of users which is constructed on the principle of hierarchical structure is offered. It is likely that the GRPC communication protocol is required to ensure the highest communication speed for synchronous HTTP requests between servers. Pay attention to the development of server parts of the adaptive mobile educational environment on the correct deployment of services, with each request by ensuring the security of data access control system through service authentication and authorization. The administrative part of the adaptive mobile educational environment is described, the service of which should be as much as possible protected from external attacks. Of particular importance is the servers that deploy the most similar data. Find more data from the server part of the mobile educational environment for creating relational databases based on MS SQL Server, as well as a documentary database based on MongoDB. Considering the capabilities of the hosting environment, a generalized scheme of deployment of backend services of adaptive mobile environment is built. As a result of designing the internal structure of systems, there are basics of relatively known and prescribed algorithms, which indicate that the infrastructure scheme is based on three main algorithms, namely: the algorithm of authentication and authorization; algorithms for interaction with the file system (storage and retrieval of medical data); algorithms for downloading and analyzing completed tasks.

Specifics of adaptive learning in the field of public governance

Research shows that today the education and training of qualified specialists in the field of public administration requires a transition from traditional to new more innovative systems of education and training. Given the fact that in the practice of education and training of highly qualified specialists in the field of public administration is actively implemented adaptive education system, which is based on the use of modern innovative teaching methods. The purpose of the scientific article is aimed at presenting the specifics of adaptive learning in the field of public administration. The scientific article uses such research methods as the method of theoretical analysis, the method of observation, the method of abstraction, the method of system analysis, the method of comparison, the method of modeling and the method of generalization. It is established that the system of adaptive learning allows to determine the individual approach and individual features of training of future specialists in the field of public administration. It is determined that in the process of adaptive learning modern Web-technologies and innovative teaching methods are actively used. It is proved that in the system of adaptive training of future specialists in the field of public administration new more innovative methods of learning, acquiring skills and abilities are used, as a result of which the confidence of future specialists to carry out professional activity increases. It was found that the main principles of adaptive training in public administration are: 1) the principle of personalization of the public administration needs, which is to train future professionals - public officials; 2) the principle of forming personal learning strategies, based on the needs of public administration in future professionals; 3) the principle of improving the thinking and professional activity of future specialists in the field of public administration, based on psychological factors of influence; 4) the principle of forming an innovative model of adaptive education and training of specialists in the field of public administration. It is established that today the Knewton training system is used in the field of adaptive learning, thanks to which it is possible to obtain information about what material the future specialists in the field of public administration have not mastered. It is determined that the main state institution of higher education in Ukraine, on the basis of which training, retraining and advanced training of civil servants (specialists in public administration) is the National Academy of Public Administration under the President of Ukraine, which provides training under the educational program "Public Administration and administration ”. It is established that as a result of successful mastering of the educational-professional program "Public administration and administration" future specialists in the field of public administration acquire the ability to form and implement public policy in various areas of public life, strategically manage social, territorial and organizational development, manage economic processes and public finances, make adjustments, proposals to existing regulations (acts), as well as prepare drafts of new regulations (acts), carry out digital governance and administrative functions and personnel management functions.

Towards Transferrable Affective Models for Educational Play

Modern computational agents in adaptive educational systems primarily rely on cognitive (i.e. curricular performance) data, while ignoring important multimodal affect cues which human tutors use to personalize their interactions with students. Students’ affective responses are highly idiosyncratic, noisy, and dependent on interactive context, challenges which defy many standard assumptions of computational player modeling. As a result, recent research efforts to model student affective response have focused on specific, single-task interactions, limiting the amount and variety of affective input from an individual player. For my thesis research, I plan to address these limitations in two ways. First, by developing a new paradigm for modeling student affective data, not as a scalar reward signal, but as a policy label, i.e., feedback on an agent’s recent behavior, and additionally by developing transfer learning methods to apply this policy feedback data across multiple game tasks. Together, these two advances may lead to more data-efficient learning and more flexible and generalizable affective models of players

Jim Greer\u2019s and Mary Mark\u2019s Reviews of Evaluation Methods for Adaptive Systems: a Brief Comment about New Goals

Mark and Greer’s (International Journal of Artificial Intelligence in Education, 4(2/3), 129–153, 1993) review was very influential in setting out effective goals and methods for evaluating adaptive educational systems of all kinds. A later review brought the story up to date (Greer, International Journal of Artificial Intelligence in Education, 26(1), 387–392, 2016). The current paper explores a new range of evaluative goals which go beyond the quality of learning outcomes, learning efficiency, transfer, retention, and short-term motivation. While learner satisfaction has been downgraded over the years as a reliable indicator of learning quality, it cannot be wholly ignored in terms of wider issues such as the learner’s developing metacognitive and meta-affective insight, regulatory competence and longer-term motivation. These factors lead on to such evaluable issues as the learner’s appetite for further learning of the kind just experienced as well as for learning in general. The rise in the use of data analytics and the increasing use of AIED and computer-based learning systems in schools and universities has led to the development of orchestration systems to assist the teacher to manage their students using such systems. Orchestration systems raise new kinds of evaluation goal, such as the balance of activity, cooperation and agency between the human teacher and the adaptive systems, as well as between the learner, the systems, the teacher and, indeed, other learners. Further evaluable goals include the degree to which the teacher is alerted to the learning difficulties of the learners, the degree to which the teacher’s scarce and valuable time is being used efficiently, and the degree to which the orchestration system can be used as a reflective device for teachers to examine their own practice.

Toward an Adaptive Educational Hypermedia System (AEHS-JS) based on the Overlay Modeling and Felder and Silverman’s Learning Styles Model for Job Seekers

In this paper we introduce a new design of an adaptive educational hypermedia system for job seekers, this proposal is based, for the part of learning objectives, on a job model which allows adapting the content and the path of education to the intended jobs, and, for the learner model construction, on a specific use of the learning styles of Felder and Silverman.
 First, we present existing literature to give a general review on adaptive edu-cational hypermedia systems, in that way; we have reported the related items to different notions in the adaptive educational Systems area as the differentiated pedagogy, the learning objects, and the learner profile. Then we argued our choice of the components of our model and we detailed the new ones.
 As designed, the model can produce a suitable learning path for the user to match the job characteristics and the learning style of the person in order to help the user owning the job sought. With the possibility of linking the required com-petencies to the education skills, we aim to map business tasks to learning activi-ties. 
 Based on this approach, we designed an Adaptive Educational Hypermedia System named AEHS-JS that will help to improve the efficiency and pragmatism of job search activities. 
 In plus of the social impact of this work as it help job seekers to complete their profiles and get the career they are looking for, this work will allow companies to find the candidates that match the job criteria sought.

Evaluating the impact of prior required scaffolding items on the improvement of student performance prediction

Recently, tracking student behavior has become a very important phase for constructing adaptive educational systems. Several researchers have developed various methods based on machine learning for better tracing students’ knowledge. Most of these methods have shown an effective estimation of student features and an accurate prediction of future performance. However, these methods recognized certain limitations since they use only the correctness of prior student responses to make predictions without paying attention to many other important student behaviors. In addition, researchers have only considered scaffolding items as a pure method of learning without having analyzed student performance at the time of answering these items. Our purpose in this article is to conduct an experiment that aims to evaluate how best to use data about the prior required scaffolding items to predict future student performance. For this reason, we proposed two separate models, namely, the first one identifies whether a student has previously required to use scaffolding items prior main question or has immediately answered it without requiring assistance. For the second model, as an improvement of model 1, our objective is to improve the student’s performance under the constraint of answering scaffolding items. The performance of our two models is evaluated against the original Performance Factors Analysis algorithm to mark differences. The results show that the two proposed models provide a positive improvement in predicting the future performance of students. Moreover, our second model can reliably increase the predictive accuracy.

Toward a hybrid machine learning approach for extracting and clustering learners' behaviours in adaptive educational system

The learning style is a vital learner's characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system's log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.

Toward a hybrid machine learning approach for extracting and clustering learners' behaviours in adaptive educational system

The learning style is a vital learner's characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system's log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.

Learners on Focus: Visualizing Analytics Through an Integrated Model for Learning Analytics in Adaptive Gamified E-Learning

During the Coronavirus pandemic, e-learning systems have proven to be an essential pillar for education. This raises to surface what many studies have addressed earlier; creating a platform that completes the traditional classroom work and maximizes the effectiveness of learning outcomes. Striving to achieve such platform, studies have considered gamifying and personalizing the educational resources for the adaptation of educational systems as per the intended learners through intensive learning analytics. But was the learner really a part of the adaptation process taking place? Learning analytics are usually designed to the course's adaptation and solely for the teachers. Thus, learning analytics in gamified adaptive educational systems involving the course, teachers and learners together are still under investigation. In this study, the Personalized Adaptive Gamified E-learning (PAGE) model is introduced to extend MOOCs by providing new satisfactory levels of learning analytics and visualization in the rich e-learning process that supports the learner's intervention in the resultant learning analytics. The proposed Learning analytics have been developed to make the necessary adaptation to the course and learner's learning flow, as well as visualizing the process and adaptation decisions to the learners. Results show a positive potential towards learning adaptation and visualization, and a necessity to provide an additional focus for the gamification concept.

Improvement of Academic Performance of Engineering Students through an Adaptive Educational Hypermedia System (AEHS)

This paper describes how to improve the academic performance of engineering university students through an Adaptive Educational Hypermedia System (AEHS). The psychological basis, learning styles and MOOMH methodology for the development of the system are exposed, which with its implementation achieves adaptability and works for students as an “intelligent tutor”, allowing them to guide their education as academic tutor. Not only shows its content that meets the needs of the student, but it is also represented in elements such as color adaptation, work tools and even academic recommendations based on the interactions that the user makes within the system, the system recognizes its pattern of use, and when the student is logged in again, it presents a friendlier interface that the student prefers use, it is wider in content and, above all, easy to use and understand. In addition, the AEHS allows to extend education allowing the assignment of more domain areas, in the field of engineering, that is, the SHAE can be adapted to various engineering specialties such as: industrial, software, telecommunications, mechanics and other.

Improvement of Academic Performance of Engineering Students through an Adaptive Educational Hypermedia System (AEHS)

This paper describes how to improve the academic performance of engineering university students through an Adaptive Educational Hypermedia System (AEHS). The psychological basis, learning styles and MOOMH methodology for the development of the system are exposed, which with its implementation achieves adaptability and works for students as an “intelligent tutor”, allowing them to guide their education as academic tutor. Not only shows its content that meets the needs of the student, but it is also represented in elements such as color adaptation, work tools and even academic recommendations based on the interactions that the user makes within the system, the system recognizes its pattern of use, and when the student is logged in again, it presents a friendlier interface that the student prefers use, it is wider in content and, above all, easy to use and understand. In addition, the AEHS allows to extend education allowing the assignment of more domain areas, in the field of engineering, that is, the SHAE can be adapted to various engineering specialties such as: industrial, software, telecommunications, mechanics and other.

Искусственная интеллектуализация в управлении процессом обучения работников предприятий

The relevance of the study is due to the fact that building an adaptive educational system that responds to environmental changes and creating the conditions for implementing individual (personalized) learning paths becomes a key challenge for modern education. The article analyzes the use of artificial intelligence in the training of employees of the enterprise, presents the benefits and experience of implementation in enterprises. It was determined that training with the help of artificial intellectualization is a deeper transformation of the entire learning process, the use of new digital tools to rethink how to teach in order to be modern. The principles developed within the framework of the proposed approach are the direction of changes in the education system.

Project R-CASTLE: Robotic-Cognitive Adaptive System for Teaching and Learning

Robots are already present in people’s lives as receptionists, caregivers, and tutors. In human–robot interaction, social behavior is not only expected but often associated with users’ confidence. Although several studies have been researching in this direction, the robot adaptation and the existing gap between the system and nonprogramming designers still need more effort to achieve success. In this article, a cognitive architecture is proposed and implemented into a humanoid robot. The aim is to offer a framework programmable for controlling the robot’s resources, approaching previous knowledge, and new content in educational interactive activities. Furthermore, the system adapts the robot’s behavior according to objective measures of users, attention and engagement during the activity. After the interactive sessions, these measures are provided in a graphical interface for students, skills evaluation. Functions of visual classification, speech processing, autonomous web search for new content, and attention detectors were tested and analyzed separately. This approach shows effectiveness in basic and medium condition levels from a set of sceneries for each module.

IMS Compliant Ontological Learner Model for Adaptive E-Learning Environments

It has been proven that adopting the “one size fits one” approach has better learning outcomes than the “one size fits all” one. A customized learning experience is attainable with the use of learner models, the main source of variability, in adaptive educational hypermedia systems or any intelligent learning environment. While such a model includes a large number of characteristics which can be difficult to incorporate and use, several standards that were developed to overcome these complexities. 
 In this paper, the proposed work intents to improve learner’s model representation to meet the requirements and needs of adaptation. We took IMS-LIP, IMS-ACCLIP and IMS-RDCEO standards into consideration and incorporated their characteristics to our proposed learner model so that it conforms to international standards. Moreover, the suggested learner model takes advantage of the semantic web technologies that offer a better data organization, indexing and management and ensures the reusability, the interoperability and the extensibility of this model. Furthermore, due to the use of ontologies, the metadata about a learner can be used by a wide range of personalization techniques to provide more accurate customization.

Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type

PurposeThrough harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type.Design/methodology/approachThe DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases.FindingsAn experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes.Originality/valueThe DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

Improving Cognitive Decision-Making into Adaptive Educational Systems through a Diagnosis Tool based on the Competency Approach

Adaptive Educational systems (AES) does not necessarily lead to a better learning. Several kinds of research reveal that the problem is due to, on the one hand, the accent is put mainly on the technological tools to the detriment of the pedagogical aspect. On the other hand, there is a lack of the importance given to the assessment which is an integral part of the learning-teaching process and the professional act of primary importance which gives the decisions and the consequences that result from it. In this paper, we propose a solution for the diagnostic evaluation based on competency approach especially on the pedagogy of integration. The proposed solution allows getting information about the available learners’ knowledge level by presenting an assessment based on the common definition given to competencies by the majority of the authors of the domain. İn this context, the assessment process proposed is presented on two steps: the first step, we evaluate resources related to the competence to verify their acquisition degree and to remediate if necessary, the second step will evaluate the capacity of leaner to mobilize those resources in order to apprehend a situation and respond to it in a more or less relevant way. This research aims to present a new vision in the context of the assessment into adaptive educational systems.

Process Model for Differentiated Instruction using Learning Analytics

Higher education institutions seem to have a haphazard approach to harnessing the ubiquitous data that learners generate on online educational platforms, despite promising opportunities offered by this data. Several learning analytics process models have been proposed to optimise the learning environment based on this learner data. The model proposed in this paper addresses deficiencies in existing learning analytics models that frequently emphasises only the technical aspects of data collection, analysis and intervention, yet remain silent on ethical issues inherent in collecting and analysing student data and pedagogy-based approaches to the interventions. The proposed model describes how differentiated instruction can be provided based on a dynamic learner profile built through an ethical learning analytics process. Differentiated instruction optimises online learning through recommending learning objects tailored towards the learner attributes stored in a learner profile. The proposed model provides a systematic and comprehensive abstraction of a differentiated learning design process informed by learning analytics. The model emerged by synthesising steps of a tried-and-tested web analytics process with educational theory, an ethical learning analytics code of practice, principles of adaptive education systems and a layered abstraction of online learning design.

Adaptive Education based on Learning Styles: Are Learning Style Instruments Precise Enough?

Investigating the efficiency of learning style instruments is significant because it is a widespread technique and it enriches the understanding of the challenges of integrating such instruments into adaptive education systems. The results showed that current learning style instruments depend only on the textual form of infor-mation to present items; this might be leading to a bias in the measurement of learning styles as the textual forms of information are more suitable for verbal students than for others. The purpose of this paper is to investigate the precision of learning style instruments and the challenges of integrating them into adaptive education systems. This research followed a quantitative research approach. First, a new learning style instrument was developed using different forms of infor-mation (Figures, Charts, and Equations). Then, the preferred learning style of fif-ty students was measured twice, initially, by using the newly developed instru-ment and subsequently by using a VARK instrument, the results of both were compared.

Learning Personalization Based on Learning Style instruments

Adaptive education systems (AES) are considered one of the most interesting research topics in technology-based learning strategies. Since students have different abilities, needs and learning styles, we should fit the curriculum and teaching activities to these different learning styles. This study investigates the impact of using LAES (Libyan Adaptive Education System) on the performance of students. An ALSI (Arabic Learning Style Instrument) was integrated into the LAES system to investigate learning preferences of students. The student models are constructed according to the results obtained using this instrument (ALSI). Three experimental studies were then conducted to investigate the impact of the LAES system on the performance of students. The results reveal the students who have learnt using the LAES system were more successful than others who learnt without, in terms of the knowledge gained.

Adaptive Design for Educational Hypermedia Environments and Bio-Educational Adaptive Design for 3D Virtual Learning Environments

AbstractAdaptive learning environments design has been originally influenced by the adaptive learning environments model, which has been recently re-shaped by the bio-educational adaptive approach. Adaptive and bio-educational models share the common main idea that education should be adaptive. Since the 90’s the adoption of an adaptive educational point of view have been at the base of adaptive educational hypermedia systems, which design joins interest towards learners’ individual differences with adaptive learning environments research. Educational hypermedia systems have been overcome by the technology of 3D Virtual Learning Environments. Some emerging questions are related to the design criteria of adaptive learning environments. Which lessons learned from adaptive hypermedia systems design could be now applied to VLEs’ design? How a virtual learning environment should be designed to be adaptive? This research tries to answer to these questions by describing Federico 3DSU, an educational University 3D Virtual Learning Environment which has been designed with adaptive criteria, according to bio-educational model