Adaptive Learning Environment Research Articles

PEDAGOGICAL FORMATION OF STUDENTS' COGNITIVE ACTIVITY IN AN ADAPTIVE EDUCATIONAL ENVIRONMENT

In modern education, the independent work of students is a necessary and important part of the educational process. Because it not only helps to consolidate the knowledge and skills acquired in practical courses but also contributes to the strengthening of cognitive activity. As a rule, one of the platforms for organizing independent work of students is an adaptive learning environment. The purpose of the study was to develop and implement a training course on the organization of independent work of students in an adaptive educational environment to increase the level of their cognitive activity. Research hypothesis: the level of cognitive activity of students increases when organizing independent work in an adaptive educational environment. By the purpose and hypothesis, the authors defined the objectives of the study: theoretical analysis of scientific literature, the study of the formation of cognitive activity of students, the study of the concepts of «pedagogical conditions» and «adaptive educational environment», diagnosis of the level of formation of cognitive activity of students; prepare students for independent work on the Moodle platform; analyze the results of the study. The proposed course included personalized adaptive learning. Second-year students of the specialty «Information Systems» of Aktobe Higher Polytechnic College in the course of mastering the subject «Mathematics» carry out training in this course. The statistics obtained indicate that the application of the developed training course in an adaptive personalized educational environment has a positive effect on increasing the level of cognitive activity of students. The data obtained by the authors can be used by teachers of other educational institutions in the process of forming the cognitive sphere of students.

Redesign of Economics Education for Creating a Flexible and Adaptive Learning Environment

The article consists of three main parts. The first part sets the framework of research demand and argues its relevance and importance. The object of the second part are the basic conditions and preconditions for effective use and acceptance of educational opportunities that the digital environment provides for educational purposes. The third part of the article presents applicable in the digital environment teaching methods in the direction of building the necessary knowledge and skills in students. The main goal of the article is to clarify and present the possibility for creative integration of digitally based models of teaching in the educational process in higher education in economics and how they are built into the system of collective and individual skills of students.

A Rasch Analysis of Students' Academic Motivation toward Mathematics in an Adaptive Learning System.

Despite the criticality of considering student academic motivation as it influences learning, research within the field of adaptive learning technologies for education has so far focused more on customising instruction to implement personalised learning, than investigating how personalised learning is associated with learners’ motivation. Given this, a robust instrument is required to gather information about student academic motivation within the context of adaptive learning technologies. This study sought to validate the Academic Motivation Toward Mathematics Survey (AMTMS) currently used to measure motivation based on self-determination theory in mathematics education at pre-tertiary levels (grades 11 and 12) in Asia. A total of 196 participants recruited via availability sampling took part in this study, after interacting with an in-house mathematics adaptive learning system within a tertiary educational institution. The validation was performed based on modern test theory given that it overcomes some limitations of classical factor analytic approaches. Results supported the factorial structure of the AMTMS but 12 of the original 21 items had to be rescored to establish ordered thresholds. Further, the bifactor equivalent solution suggested the possibility of reporting a singular motivation index comprising the five factors within the AMTMS. Along with the results, this study offers researchers a robust and validated instrument to measure motivation toward mathematics that can be used within an adaptive learning environment.

OntoStrength: An Ontology for Psychomotor Strength Development

An ontology is a formal, explicit description of concepts and relations from a domain while considering underlying properties, restrictions, and instances.With the advancement of the Semantic Web, the rise of the Educational Semantic Web, and the lack of uniformity between approaches for knowledge representation, ontologies are becoming more and more popular, including adaptive learning environments such as the Intelligent Tutoring Systems (ITSs). OntoStrength is an ontology developed to support the Selfit ITS, a platform that aims to improve the fundamental human psychomotor skills and, more specifically, bio-motor strength abilities. The goal of Selfit is to prevent the negative consequences of a sedentary lifestyle and accidents involving inadequate strength skills. Most ontologies in the sports domain support the development of digital solutions for sports performance and data collected during competitions. In contrast, OntoStrength’s goal is to contribute to the development of digital solutions dedicated to bio-motor strength ability analysis. OntoStrength considers other bio-motor skills like speed, endurance, or flexibility, as well as other activities like muscle analysis, movement patterns, or training load management, to support sport, professional and daily-life activities. OntoStrength enables the personalization of strength development programs in the Selfit ITS by providing a comprehensive data layer for its student, domain, and tutoring models.

Adaptive Assessment and Content Recommendation in Online Programming Courses: On the Use of Elo-rating

Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students’ development and engagement with programming is a challenging process. Early career computer science professionals are required not only to understand and master numerous programming concepts but also to efficiently learn how to apply them in different contexts. A prerequisite for an effective and engaging learning process is the existence of adaptive and flexible learning environments that are beneficial for both students and teachers. Students can benefit from personalized content adapted to their individual goals, knowledge, and needs; while teachers can be relieved from the pressure to uniformly and promptly evaluate hundreds of student assignments. This study proposes and puts into practice a method for evaluating learning content difficulty and students’ knowledge proficiency utilizing a modified Elo-rating method. The proposed method effectively pairs learning content difficulty with students’ proficiency, and creates personalized recommendations based on the generated ratings. The method was implemented in a programming tutoring system and tested with interactive learning content for object oriented-programming. By collecting quantitative and qualitative data from students who used the system for one semester, the findings reveal that the proposed method can generate recommendations that are relevant to students and has the potential to assist teachers in grading students by providing a more holistic understanding of their progress over time.

Adaptive Learning Techniques for a Personalized Educational Software in Developing Teachers’ Technological Pedagogical Content Knowledge

The authors discuss the design and utilization of e-TPCK, a self-paced adaptive electronic learning environment designed to promote the development of teachers’ Technological Pedagogical Content Knowledge (TPCK). The system employs a technological solution that promotes teachers’ ongoing TPCK development by engaging them in personalized learning experiences using technology-infused design scenarios. Results from an experimental research study revealed that the experimental group outperformed the control group in developing TPCK on two design tasks, substantiating the claim that the use of technology can better facilitate the development of TPCK than traditional instruction. Furthermore, e-TPCK’s adaptive guidance using worked-out examples led to mathemagenic learning effects for novice student-teachers, while for the more experienced student-teachers learning with e-TPCK proved beneficial only at the beginning of their engagement with the system. Hence, the relative effectiveness of instructional techniques embedded in e-TPCK became less effective as a result of student-teachers’ increasing expertise, providing evidence for the expertise reversal effect. In conclusion, the findings of the research are encouraging for the advantages of personalized learning in teacher education and showed that the design of adaptive learning technologies must account for learners’ prior knowledge and changing levels of expertise throughout the ongoing interaction with the computer system.

Multi-Label Classification and Explanation Methods for Students’ Learning Style Prediction and Interpretation

The current paper attempts to describe the methodology guiding researchers on how to use a combination of machine learning methods and cognitive-behavioral approaches to realize the automatic prediction of a learner’s preferences for the various types of learning objects and learning activities that may be offered in an adaptive learning environment. Generative as well as discriminative machine learning methods may be applied to the classification of students’ learning styles, based on the student’s historical activities in the e-learning process. This paper focuses on the discriminative models that try to learn which input activities of the student(s) will correlate with a particular learning style, discriminating among the inputs. This paper also investigates several interpretability approaches that may be applicable for the multi-label models trained on non-correlated and partially correlated data. The investigated methods and approaches are combined in a consistent procedure that can be used in practical learning personalization.

How universities have responded to E-learning as a result of Covid-19 challenges

E-learning environments designed with adaptive technology in mind can help students learn and retain information more effectively by enhancing their learning experience and increasing their level of engagement. Here, students' learning styles are considered in creating an adaptable online environment, and the effects on student engagement are examined. For the sake of this study, we've also attempted to describe and compare the suggested adaptive learning environment to an existing e-learning technique. With Covid-19 in the classroom, technology advancements have grown exponentially, and this progress has coincided with the process of teaching and learning. Virtual classrooms necessitated an e-learning process since it was the most user-friendly teaching method. A descriptive, correlative, transversal and prescriptive research approach was used. An online survey was used to gather data to get a random and voluntary sample of 3560 university students from Peru. A lack of reading comprehension is an issue, but students may use communication technologies and the Internet to improve their teaching and to learn via self-learning.

Artificial Intelligent in Education

The application of Artificial Intelligence or AI in education has been the subject of academic research for more than 30 years. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. Nowadays, there are several new challenges in the field of education technology in the era of smart phones, tablets, cloud computing, Big Data, etc., whose current research questions focus on concepts such as ICT-enabled personalized learning, mobile learning, educational games, collaborative learning on social media, MOOCs, augmented reality application in education and so on. Therefore, to meet these new challenges in education, several fields of research using AI have emerged over time to improve teaching and learning using digital technologies. Moreover, each field of research is distinguished by its own vision and methodologies. In this article, to the authors present a state of the art finding in the fields of research of Artificial Intelligence in Education or AIED, Educational Data Mining or EDM and Learning Analytics or LA. We discuss their historical elements, definition attempts, objectives, adopted methodologies, application examples and challenges.

Creating sustainable Posthuman Adaptive Learning environments for pregnant teenagers

abstract Posthuman theorisation provides us with the conceptual tools to analyse and to understand how sustainable learning environments (SuLE) are created through adaptive learning (AL) as a form of artificial intelligence (AI) and as an aspect of a broader collective of relationalities. In this study our focus is on how pregnant teenagers relate to the curriculum, one another, other learners, parents, teachers, schools, communities, and non-human and more-than-human entities as they learn. Their condition currently makes them vulnerable and places them in less powerful positions to influence their learning in ways that align with their abilities and modes of being. The Posthumanist lens assists moves away from socialised gender, racial or generally underclass categories and dispositions. It enables us to situate pregnant teenagers’ feminine subjectivities beyond Humanism’s representations of this demographic as bearing-stigma, facing exclusion and marginalisation. This mode of seeing enables the possibility of re/imagining the pregnant teenager’s experiences through modes of being in which participation in networks and collaborations through adaptive learning, among others, draw on pedagogic technologies of change. We argue for a dissolving of Humanist barriers that define, stigmatise and burden the pregnant teenagers as they are fully integrated in their relationalities as learners in AI learning networks. Access to AL and similar software and gadgets need to be massified and opened up for use by all, irrespective of gender, socio-economic status, religion or any marginalising marker.

AI-assisted knowledge assessment techniques for adaptive learning environments

The growth of online learning, enabled by the availability on the Internet of different forms of didactic materials such as MOOCs and Intelligent Tutoring Systems (ITS), in turn, increases the relevance of personalized instructions for students in an adaptive learning environment. There are increasing interests as well as many challenges in the application of Artificial Intelligence (AI) techniques in educational settings to provide adaptive learning content to learners. Knowledge assessment is necessary for providing an adaptive learning environment. A student model serves as a fundamental building block of knowledge assessment in an adaptive learning environment. This paper intends to review the development of dominant families of student models with psychometric theory in early educational research, recent adaptations, and advances with machine learning and deep learning techniques. Our review covers not only the important families of student models but also why they were invented from both theoretical and practical viewpoints with AI and educational perspectives. We believe that the discussion covered in this review will be a valuable reference of introductory insights to AI for educational researchers, as well as an endeavor of introducing basic psychometric perspectives to AI experts for knowledge assessment in the field of learning science. Finally, we provide recent challenges and some potential directions for developing efficient knowledge assessment techniques in future adaptive learning ecosystems.

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner’s performance and paves way for offering personalized learning experiences.

Real-time prediction of science student learning outcomes using machine learning classification of hemodynamics during virtual reality and online learning sessions

Current data sources used for the prediction of student outcomes average about 55% accuracy and require a significant amount of input data and time for researchers and educators to produce predictive models of student outcomes. The aim of this study is to examine how neurocognitive data collected via functional near infrared spectroscopy (fNIRS) may be used to create predictive models of student outcomes with greater speed and accuracy when using a synthetic adaptive learning environment (SALEs). Specifically, this study examines the utility of using neurocognitive data to develop student response prediction on a science content test. Participants were recruited from schools located in the United States ( n = 40). Participants in the study engaged in three conditions: no content, video and virtual reality. The lesson video and virtual reality lesson provides an explanation of deoxyribonucleic acid replication. Observed neurocognitive responses were collected during each condition and used to predict the success of student responses on an assessment. On average the predicative accuracy of this approach is 85% and occur within 300 ms. Predictive error rates are less than 15%. Results of this study provides evidence to support the use of neurocognitive data for adaption of digitally presented content and how machine learning approaches and artificial intelligence may be used to classify student data in real-time as students engage with content. Results also illustrate good accuracy and capture of moment-to- moment fluctuations of cognition in real-time. These findings may help the development of artificially intelligent tutors and improve student-based learning analytics. • Classification of neurocognitive data lends itself to prediction of student outcomes. • Describes neurocognitive data sources for development of predictive learning platforms. • Hemodynamic response can help to understand levels of student attention and engagement.

Effectiveness of Adaptive electronic learning environment based on different patterns of educational preferences (individual - group) in developing skills of electronic course design and production in accordance with quality standards for students education technology Department students

Effectiveness of Adaptive electronic learning environment based on different patterns of educational preferences (individual - group) in developing skills of electronic course design and production in accordance with quality standards for students education technology Department students

Leveraging eye tracking to understand children’s attention during game-based, tangible robotics activities

The difficulty in maintaining attention can interfere with the acquisition of critical academic skills. Recently, researchers have used embodied and game-based learning to support skill acquisition for children with learning difficulties. In this context, robots can be an interesting asset to foster engagement and investigate game dynamics. However, it is still an open question of how to develop adaptive learning environments for children with learning difficulties. Before one can provide effective adaptation, a first step is needed to understand the differentiating behaviors during the activity for children with attention difficulties. Three such differentiating behaviors are how a child divides his or her attention during the learning activity, the child’s level of cognitive load, and the child’s physiological fatigue, which are the focus of our study Using a robot assisted, gamified activity, we conducted a user study with 18 children having difficulty in maintaining attention. Using process mining techniques and eye-tracking data, we found the importance of integrating the autonomous robots into the attention patterns to successfully complete a game and the influence their behaviors can have on the participant’s attention. This importance was supported by the cognitive load of participants decreasing the more they focused on the autonomous robots in successful games. This work contributes to the understanding of children’s behaviors during tangible game-based activities and can be used to build effective adaptation for children with attention difficulties.

Adaptive learning environment design in the system of future maritime specialits’ training

In an environment of education reformation aimed at transition of higher education to competence based and individual approaches, we face the need to construct individual learning path for every future maritime professional. In this respect technology of adaptive learning based on modern ICT becomes of high importance. At the same time COVID-19 pandemic has changed system of education at all its levels, but the issue of quality and efficiency is still to be considered and studied by scientists and practitioners. Under these conditions the issue of adaptive information environment creation becomes relevant for training modern and competitive specialists. This environment should be based on implementation of adaptive technologies for education and training of maritime students, therefore, article provides investigation of pedagogical problem of future navigators’ professional culture building in training system of adaptive information environment of maritime educational establishment. Feasibility of adaptive learning technologies implementation is grounded as a tool for future navigators’ professional culture building in the process of their fundamental education and training. Example of higher mathematics adaptive learning implementation for future navigators at Kherson State Maritime Academy is considered. Higher mathematics adaptive learning was introduced through: adaptive feeding of educational content of the course; problems solving support based on examples and pre-created typical algorythms; adaptive testing; analysis of test tasks answers; system teacher support; constant support conditions for individual tasks completion; adaptive course navigation, etc. As the result of experiment there was found out that higher mathematics adaptive learning for future navigators presupposes: individual learning path designing; possibility to timely provide advising and objective control as well as evaluation; enhancement of learning activity and motivation of through improved degree of autonomy; promotion of students’ research skills development; creation of cooperation, partnership and maritime brotherhood atmosphere.

An enhanced Elo-based student model for polychotomously scored items in adaptive educational system

ABSTRACT The adaptive learning environment provides learning support that suits individual characteristics of students, and the student model of the adaptive learning environment is the key element to promote individualized learning. This paper provides a systematic overview of the existing student models, consequently showing that the Elo rating system has greater potential as compared to the other models regarding application in the online learning environment. Based on the Elo model, this study proposes the EELO, an enhanced Elo rating system, in consideration of the application scenarios of polychotomously scored items and multi-dimensional granularity evaluations not covered by the basic Elo rating system. The EELO model estimating students’ cognitive abilities and predicting their future performances on unknown questions is evaluated based on one public set (Assigment2) and one proprietary dataset (HSK), and achieved an AUC of 0.92 for Assigment2 and 0.84 for HSK, which shows that the EELO model has the best performance regarding the above-mentioned objectives as compared with the latest extensions of the IRT and BKT models. Subsequently, the EELO model was tested and applied successfully in a real large-scale online learning environment to demonstrate the potential of the EELO model in adaptive learning applications.

Artificial Intelligence Applications with e-Learning System for China’s Higher Education Platform

Artificial Intelligence (AI) assisted educational institutions extensively utilize electronic learning context to guarantee improved teaching and learning experiences accompanied by educational activities. E-learning or online learning plays a significant role in Chinese higher education. There is a challenge to implement e-learning in China’s higher education to improve course resources, student learning style prediction, teaching quality, and service support. Hence in this paper, Artificial Intelligence based Efficient E-learning Framework (AI-EELF) has been proposed to overcome the challenges faced by China’s higher education while implementing e-learning modules. The collected student data can be efficiently utilized and exploited to progress in an adaptive learning environment. The proposed AI-EELF method introduces multiple learning models to enhance teaching quality and predict the student learning style. The experimental results show that the proposed AI-EELF achieves high performance, prediction ratio in determining students’ learning style and improves teaching quality compared to other existing methods.

An adaptive 3D virtual learning environment for training software developers in scrum

ABSTRACT Scrum is one of the most used frameworks for agile software development because of its potential improvements in productivity, quality, and client satisfaction. Academia has also focussed on teaching Scrum practices to prepare students to face common software engineering challenges and facilitate their insertion in professional contexts. Furthermore, advances in learning technologies currently offer many virtual learning environments to enhance learning in many ways. Their capability to consider the individual learner preferences has led a shift to more personalised training approaches, requiring that the environments adapt themselves to the learner. We propose an adaptive approach for training developers in Scrum, including an adaptive virtual learning environment based on Felder's learning style theory. Although still preliminary, our findings show that students who used the environment and received instruction matching their preferences obtained sightly higher learning gains than students who received a different instruction than the one they preferred. We also noticed less variability in the learning gains of students who received instruction matching their preferences. The relevance of this work goes beyond the impact on learning gains since it describes how adaptive virtual learning environments can be used in the domain of Software Engineering.

A profile analysis of online assignment motivation: Combining achievement goal and expectancy-value perspectives

Online assignment is a global and growing phenomenon in higher education that presents significant motivational challenges for many online students. Yet, little is known on the motivational beliefs of students towards online assignment, and none on how in combination they affect student behavior when it comes to online assignment. This present study aims to identify online assignment motivation profiles based on achievement goal theory (mastery- and performance-approach goals) and expectancy-value theory (expectancy and value components). Latent profile analysis (LPA) was used to examine if homogenous latent profiles exist within a sample of 612 college students in China. Our findings identified four distinct profiles: High Motivation, Moderate Motivation, Low Goal Orientation/Moderate Expectancy-Value, and Very High Goal Orientation/Very Low Expectancy-Value. Our findings further revealed that profile membership was associated with self-regulation of online assignment behavior, including handling distraction, arranging the environment, managing time, monitoring motivation, emotion management, and cognitive reappraisal. Finally, supplemental analyses showed the added benefit of integrating both achievement goal and expectancy-value theories when forming online assignment motivation profiles. These findings indicated the importance of promoting multiple motivational beliefs to better support self-regulation of online assignment behavior. The present study suggests that understanding online assignment motivation profiles can provide new insights into promoting an adaptive online learning environment and help online instructors enhance and sustain their students’ motivation with online assignment (e.g., to provide differentiated support based on the motivational needs of online students in each profile).