Adaptive Learning Applications Research Articles

Adaptive learning through technology: a technical review and implementation

PurposeThere is an increase globally of students using technology to support their learning. The purpose of this paper is to outline the technical aspects of adaptive learning and contribute to the development of pedagogy that incorporates this method in teaching and learning.Design/methodology/approachThis is a technical review article that summarises key guidance on the application of adaptive learning and then reflects on its application in a UK and Vietnamese context.FindingsInitial analysis demonstrates that learning can occur asynchronously because of students engaging with adaptive learning. Issues and recommendations were derived from the reflections and practice of both UK and Vietnamese practitioners. Recommendations focussed on the more practical elements of constructing and maintaining adaptive learning. Questions were then constructed to make the decision of whether to implement adaptive learning into teaching and learning practices.Originality/valueThis academic commentary reflects on the implementation of asynchronous learning adaptive technologies in both the UK and Vietnam, specifically exploring the use of a “mastery path” and “computerised adaptive testing” to enhance student understanding.

Transgressing local, national, global spheres: the blackboxed dynamics of platformization and infrastructuralization of primary education

ABSTRACT This article analyzes how platformization and infrastructuralization are currently reshaping the educational sector by engaging in ‘sphere transgressions’, resulting in the merging of a local and national public sector into a transnational and global digital market. It elaborates on the adaptive learning application Bingel as a case-in-point to exemplify how sphere transgressions are conducive to data accumulation across national markets and sectors into transnational and global data infrastructures. Zooming in on these processes as ‘sphere transgressions’ we ask: how are local student data becoming prime assets in the global flow of digital resources? How does this benefit the financial basis of tech firms rather than serving the need for openness and transparency of educational institutions? The conclusion expands on the implications of these sphere transgressions for the future of national education as a public good.

Understanding the Digital Companions of Our Future Generation

The main protagonist in Kazuo Ishiguro’s latest novel is Klara, an artificial friend whose existential goal is to be a child’s companion. Some aspects of this fictional narrative have begun to gradually enter our daily lives. Products reminiscent of Klara are available abundantly on the market: smart toys, adaptive learning applications, and companion robots. Children can relate to these products and perform activities together with them. Preliminary research has shown fundamental differences between existing technologies and these emerging children’s digital companions. However, we still do not know much about their benefits and risks. This paper explores different and even contradicting perspectives on the phenomenon. We present the discussion from four perspectives - 1) temporality, 2) use, 3) trust and ethics, and 4) sociotechnical design - and conclude the paper with an agenda for interdisciplinary IS research. The agenda points to the need for a psychological, medical, engineering, and temporal research community to understand this emerging sociotechnical phenomenon and design its future for the better.

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.

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

Currently, data are a very valuable resource for organizations. Through analysis, it is possible to profile people or obtain knowledge about an event or environment and make decisions that help improve their quality of life. This concept takes on greater value in the current pandemic, due to coronavirus disease 2019 (COVID-19), that affects society. This emergency has changed the way people live. As a result, the majority of activities are carried out using the internet, virtually or online. Education is not far behind and has seen the web as the most successful option to continue with its activities. The use of any computer application generates a large volume of data that can be analyzed by a big data architecture in order to obtain knowledge from its students and use it to improve educational processes. The big data, when included as a tool for adaptive learning, allow the analysis of a large volume of data to offer an educational model based on personalized education. In this work, the analysis of educational data through a big data architecture is proposed to generate learning based on meeting the needs of students.

Mobile and Adaptive Learning Application for English Language Learning

Mobile learning is considered a new phase of e-learning which gives the opportunity to learn more effectively and efficiently. In addition, the use of mobile devices for leaning is more sophisticated and more useful. The m-learning has become available anywhere and anytime for all students and professors. Moreover, the features of these mobile devices include ease of use in every place and time, very reasonable cost for students, and the ability to communicate through the internet or mobile networks, encouraging the development of many kinds of methods and systems. Now, a huge number of applications, in several domains, are oriented to this kind of mobile device. Researchers have been exploiting this technology to enhance the knowledge of learner (especially foreign languages learning). In this paper, the author proposed an educational system that provides the opportunity for students to learn English language outside the classroom and encourages them to get actively involved in their own learning processes.

Mobile and Adaptive Learning Application for English Language Learning Using the SPSS Method

Mobile and Adaptive Learning Application for English Language Learning Using the SPSS Method

E-Learning Model to Support Industrial Based Adaptive Learning for Student Vocational High School

Vocational High Schools have an important role in improving the quality of human resources to meet industrial needs. However, the current education system in Vocational Schools especially in Purwakarta, Subang, Karawang, Bekasi (PURWASUKASI) still has disadvantages compared to other countries. The government through the ministry of industry has started vocational education for Vocational Schools in Indonesia. Vocational education conducted to support the creation of qualified human resources that can meet the industrial needs related to labor. The implementation of information technology in the field of education is one of the ways to improve the quality of education, including education in vocational schools. E-learning implementation aims to improve the quality of education. E-learning is often used in the application of adaptive learning in the field of education. In this study, an e-learning system model has been created that has been developed to support industrial based adaptive learning for Vocational High Schools in PURWASUKASI. The system that has been developed has been tested from the functional side of the system and shows that the developed e-learning system is ready to use.

The Adaptive Learning Landscape

BackgroundPersonalizing education by adapting learning opportunities and instructional practices to individual abilities and dispositions has been a long-standing objective among educators and indeed, among all who seek more powerful learning experiences. Interest in adapting learning opportunities to meet the needs of learners has continued throughout history up to the present time.PurposeThis article considers both historical and contemporary work to create adaptive learning opportunities to illustrate the various strands of thought about the personalization of learning experiences and to identify active lines of relevant research and development activities.Research DesignA wide range of work to create adaptive learning applications both within the education sector and the research community and beyond is reviewed. The review is organized according to a model of the processes leading to adaptive learning opportunities that calls attention to seven categories of work encompassing everyday tasks, learning tasks, learning theory, assessment, curriculum, teaching, and networking activities.ConclusionsAfter noting some promising directions for additional work, the paper concludes by identifying issues that should be considered in the further development of adaptive learning applications to avoid potential negative effects.

Empirical Calibration of Adaptive Learning

Adaptive learning introduces persistence in the evolution of agents’ beliefs over time. For applied purposes this is a convenient feature to help explain why economies present sluggish adjustments towards equilibrium. The pace of learning is directly determined by the gain parameter, which regulates how quickly new information is incorporated into agents’ beliefs. We document renewed empirical calibrations of plausible gain values for adaptive learning applications to macroeconomic data. We cover a broad range of model specifications of applied interest. Our analysis also includes innovative approaches to the endogenous determination of time-varying gains in real-time, and a thorough discussion of the different theoretical interpretations of the learning gain. We also evaluate the merits of different approaches to the gain calibration according to their performance in forecasting macroeconomic variables and in matching survey forecasts. Our results indicate a great degree of heterogeneity in the gain calibrations according to the variable forecasted and the lag length of the model specifications. Calibrations to match survey forecasts are found to be lower than those derived according to the forecasting performance, suggesting some degree of bounded rationality in the speed with which agents update their beliefs.

The First Adaptive MOOC: A Case Study on Pedagogy Framework and Scalable Cloud Architecture—Part I

Abstract The opportunity to learn from the best educators of the top universities at no cost has captured the imagination of millions who have registered for MOOCs across the United States and abroad. As MOOCs have gained momentum, critical debate is brewing on the pedagogical effectiveness of MOOCs. The high attrition rate of students who register at the beginning of a MOOC is a major cause of concern regarding the long-term success, impact, and sustainability of MOOCs. Having conducted many experiments and studies on the application of adaptive learning in personalized online learning, Synaptic Global Learning set out to develop the first adaptive MOOC (aMOOC) platform, providing a strong pedagogical framework and a personalized learning experience in a MOOC learning environment. In partnership with the Center for Innovation and Excellence in eLearning of the College of Advancing and Continued Studies, University of Massachusetts Boston, Synaptic Global Learning developed the first aMOOC in the area of ...