Adaptive Learning Experiences Research Articles

Revolutionizing Education: Artificial Intelligence's Pioneering Role in Shaping Tomorrow's Scholars

The integration of AI technologies into education represents a seismic shift, ushering in an era of personalized and adaptive learning experiences. Machine learning algorithms, with their ability to process vast amounts of data, enable the customization of educational content according to individual student needs. This tailored approach not only caters to diverse learning styles but also cultivates critical thinking, creativity, and problem-solving skills essential for the challenges of the 21st century. One of AI's significant contributions is its ability to create inclusive learning environments. Through speech recognition and natural language processing, AI ensures that education is accessible to all, breaking down barriers for students with disabilities and different learning abilities. Moreover, AI-driven applications facilitate real-time feedback, enhancing student-teacher interactions and fostering a collaborative learning atmosphere. Beyond the confines of traditional classrooms, AI enriches education through immersive technologies like virtual and augmented reality. These innovations transport students to historical events, scientific phenomena, and cultural landmarks, offering unparalleled learning experiences that stimulate curiosity and engagement. Intelligent tutoring systems, empowered by AI, provide interactive guidance, adapting to individual student progress and promoting self-directed learning. However, the integration of AI in education is not without its challenges. Ethical considerations, data privacy, and ensuring equitable access to technology demand careful attention. Collaborative efforts among educators, policymakers, and technologists are crucial to harnessing AI's full potential responsibly. In essence, "Empowering Tomorrow's Minds" paints a visionary picture of education where AI is not just a tool but a catalyst, nurturing a generation of lifelong learners equipped with the skills and knowledge to navigate an ever-changing world. Through thoughtful integration and collaborative endeavours, AI is shaping the evolution of education, empowering minds and paving the way for a brighter, more inclusive future.

FGPE+: The Mobile FGPE Environment and the Pareto-Optimized Gamified Programming Exercise Selection Model—An Empirical Evaluation

This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progressive Web Applications (PWAs) within the education sector, specifically for programming education. Despite the evident lack of recognition of PWAs in this arena, we present an innovative approach through the Framework for Gamification in Programming Education (FGPE). This framework takes advantage of the ubiquity and ease of use of PWAs, integrating it with a Pareto optimized gamified programming exercise selection model ensuring personalized adaptive learning experiences by dynamically adjusting the complexity, content, and feedback of gamified exercises in response to the learners’ ongoing progress and performance. This study examines the mobile user experience of the FGPE PLE in different countries, namely Poland and Lithuania, providing novel insights into its applicability and efficiency. Our results demonstrate that combining advanced adaptive algorithms with the convenience of mobile technology has the potential to revolutionize programming education. The FGPE+ course group outperformed the Moodle group in terms of the average perceived knowledge (M = 4.11, SD = 0.51).

Educational Technology in Teaching Community: Reviewing the Dimension of Integrating Ed-Tech Tools and Ideas in Classrooms

Particularly in light of the COVID-19 epidemic, technology is a catalyst for changing teaching and learning, not just a tool for providing information. This essay aims to examine the three ways that personalization, relevance, and skill development might affect the teaching profession. This research is a qualitative research and library research. The capacity of educators to adapt learning experiences to the needs, interests, and preferences of each student is referred to as personalization. By giving users access to data, material, and the cloud, technology may enable personalization and allow teachers to track student progress, differentiate their lessons, and give feedback. Technology can enable students to take control of their education and choose their own speed and direction. Relevance is the relationship between what students are learning and their personal objectives, ambitions, and aspirations. By establishing blended learning settings that include online and offline activities, real-world challenges, and realistic assignments, technology may increase relevance. Students may engage with classmates and professionals, access materials outside of the classroom, and apply what they have learned in many circumstances with the use of technology. The development of students' skills includes preparing them for the workforce and culture of the twenty-first century. By introducing kids to contemporary technology tools and abilities including coding, programming, physical computing, and computational thinking, technology may promote skill development. Through maker education and STEAM (science, technology, engineering, arts, and mathematics) education, technology can also encourage students to develop their problem-solving, critical thinking, collaborative, and creative skills.

The Potential of Artificial Intelligence in Improving Linguistic Competence: A Systematic Literature Review

As AI technology develops, we can expect to see more innovative and effective language-learning tools emerge in the future. These tools have the potential to revolutionize the way we learn languages, making them more accessible and enjoyable for learners of all ages and abilities. AI-powered language learning tools can give learners access to a variety of resources, such as dictionaries, grammar guides, and sample texts. It helps learners to broaden their knowledge of the language and to improve their comprehension skills. This study aimed to carry out a systematic review exploring the potential of artificial intelligence in improving linguistic abilities. The literature search process was carried out on various databases (PubMed, Web of Sciences, and Google Scholar) regarding the analysis of artificial intelligence can improve linguistic competence. This study follows the preferred reporting items for systematic reviews and meta-analysis (PRISMA) recommendations. The use of AI in improving linguistic competence has great potential. Through personalized and adaptive learning experiences, interactive and immersive learning environments, instant feedback, and access to a wide range of resources, AI can help individuals broaden their language knowledge, improve comprehension, and hone their speaking skills.

Strengthening Resilience in Refugee Children Through Art and Attention to their Voices

The study focuses on refugee children living in a temporary transit camp in Lesbos, Greece, which endeavors to provide the children with safety, security, and an adaptive learning experience under transit camp conditions. The research question was: Can children develop a high level of hope during prolonged hardship and distress? The study adopted the salutogenic approach developed by Antonovsky, according to which resilience can develop under severe stress. We also used an arts-based research method to focus on how a sense of coherence can enhance an overall sense of hope. Findings confirm that hope is a component of inner resilience, and not necessarily dependent on external circumstances, even when those circumstances are harsh and threatening. Additionally, it appears that art can enhance children’s ability to communicate their social context, as well as the capacity of adults to pay attention to the children’s voices. The findings support the argument that hope is a personality trait, and not necessarily dependent on external circumstances. The findings obtained from the children’s drawings and class discussions enhance our understanding that a positive experience of support can be achieved in a protected environment, where clear boundaries are set, and attention is given to forming positive relationships.

Marketing Education Renaissance Through Big Data Curriculum: Developing Marketing Expertise Using AI Large Language Models

Utilising big data sources and artificial intelligence (AI) tools with marketing activities and analysis contrast with questionnaires and small n observations, essentially creating a renaissance in marketing education. As a result, marketing education keeps pace with AI developments and ensures learners (or students) prepare for the demands of the modern marketing landscape 2025-30. The authors advocate a central focus on a big data-driven marketing curriculum for marketing education. Such a curriculum places AI and machine learning center stage to help understand, analyze and utilize large and complex marketing datasets for predictive marketing. In doing so, the potential exists for practitioners to link marketing strategy directly with marketing execution, allowing learners to use big data and AI for upstream strategy design and marketing plan development while downstream predicting the results of marketing campaigns, programs, and initiatives But necessary changes in pedagogy are creating adaptive learning experiences breaking free from traditional assessments In our model of learning educators enable the development of practical marketing expertise using the techniques and tools of micro-testing to nudge learners using Python data science notebooks. Overall, a renaissance in marketing education is made possible with a focus on a big data AI tools-driven curriculum. Such attention ensures learners prepare for the demands of the modern marketing landscape, moving well beyond marketing analytics using the AI technologies of Large Language Models, further expanding the use of big data Learners use role play, witnessing firsthand experiences fulfilling new hitherto emerging marketing roles By 2025, Educators fostering a big data AI-focused marketing education curriculum ensure the next generation of AI marketers will eagerly shape the future of marketing practice and behavior with new roles combining human work with AI.

AI Lights and Shadows: Revolutionizing the World

The article examines the effects of artificial intelligence (AI) by highlighting its advantages and its uses, as well as the difficulties and worries it raises. The benefits of AI are emphasized in the first section across several industries, including healthcare, transportation, and education. AI has completely changed the healthcare industry by providing more precise and quick diagnosis, individualized treatment regimens, and better patient care. With the help of traffic management systems and self-driving automobiles, artificial intelligence (AI) technologies have the potential to improve safety, efficiency, and sustainability in transportation networks. Artificial Intelligence (AI) in education provides immersive learning environments, improved learning outcomes, and tailored and adaptive learning experiences. The difficulties and worries surrounding AI are covered in detail in the section that follows. With worries about algorithmic bias and the lack of transparency in sophisticated AI algorithms, ethical considerations are crucial. Another worry is job displacement since the possibility of automation raises concerns about the nature of labor in the future. The gathering and processing of vast volumes of personal data raises the possibility of security and privacy problems. It is essential to address these issues to guarantee the proper development and application of AI systems.

Modeling Learners to Early Predict Their Performance in Educational Computer Games

Data mining approaches have proven to be successful in improving learners’ interaction with educational computer games. Despite the potential of predictive modelling in providing timely adaptive learning and gameplay experience, there is a lack of research on the early prediction of learners’ performance in educational games. In this research, we propose an early predictive modelling approach, called GameEPM, to estimate learners’ final scores in an educational game for promoting computational thinking. Specifically, the GameEPM approach models the sequence of learners’ actions and then uses a limited sequence of the actions to predict the final score of the game for each learner. The findings from our initial trials show that our approach can accurately and robustly estimate the learners’ performance at the early stages of the game. Using less than 50% of learners’ action sequences, the cross-validated deep learning model achieves a squared correlation higher than 0.8 with a relative error of less than 8%, outperforming a range of regression models like linear regression, random forest, neural networks, and support vector machines. An additional experiment showed that the validated deep learning model can also achieve high performance while tested on an independent game dataset, showing its applicability and robustness in real-world cases. Comparing the results with traditional machine learning methods revealed that, in the validation and application phases, up to 0.30 and 0.35 R2 gain is achieved in favor of the deep learning model, respectively. Finally, we found that while the lengths of action sequences influence the predictive power of the traditional machine learning methods, this effect is not substantial in the deep learning model.

Trends and issues of immersive learning environments in higher education from 2001 to 2020: perspectives on adaptive ubiquitous learning experiences

Trends and issues of immersive learning environments in higher education from 2001 to 2020: perspectives on adaptive ubiquitous learning experiences

Trends and issues of immersive learning environments in higher education from 2001 to 2020: perspectives on adaptive ubiquitous learning experiences

Trends and issues of immersive learning environments in higher education from 2001 to 2020: perspectives on adaptive ubiquitous learning experiences

RE-IMAGINING THE ROLE OF TECHNOLOGY IN TEACHING AND LEARNING: A CATALYZER TO BREAK THE BOUNDARY BETWEEN EDUCATORS AND STUDENTS IN EDUCATION

Ponte Academic JournalFeb 2022, Volume 78, Issue 2 RE-IMAGINING THE ROLE OF TECHNOLOGY IN TEACHING AND LEARNING: A CATALYZER TO BREAK THE BOUNDARY BETWEEN EDUCATORS AND STUDENTS IN EDUCATIONAuthor(s): Munienge Mbodila ,Clever NdebeleJ. Ponte - Feb 2022 - Volume 78 - Issue 2 doi: 10.21506/j.ponte.2022.2.5 Abstract:Technology can be a powerful tool for transforming learning and teaching inside or outside the classroom. Effective and appropriate use of technology can help sustain and advance relationships between educators and students, reinvent learning and teaching approaches and collaboration, shrink long-standing equity and adapt learning experiences to meet the needs of all learners. Universities around the globe in general and South Africa in particular, are striving to implement various educational technology tools such as e-learning, Moodle, virtual classrooms, TedEd and many others to enhance learning and teaching practice. However, to realize fully the benefits of technology in the education system, educators need to use technology effectively in their practice, be collaborators in learning, seek new knowledge, constantly acquire new skills alongside their students and build a community of practice among them. Walter Sisulu University (WSU) as an incubator of exploration and innovation has designed a ‘grassroots event’, to allow educators, academic staff, students and educational technology specialists to showcase their technology use in learning and teaching and share best practices. This paper presents the findings of a grassroots event conducted in 2018, at WSU, Queenstown Campus and report on the impact, usefulness of the event as well as the expectations on the use of technology for teaching and learning. The paper has an implication to stakeholders in higher education who include: faculty, educators, researchers, policymakers, funders, e-learning specialists and academic developers who are responsible for Professional Academic Development (PAD). Download full text:Check if you have access through your login credentials or your institution Username Password

542 Rethinking/reexamining Grand Challenges Within Teaching for Workforce Readiness – a Scorecard and Recalibration

Abstract The ASAS Public Policy Committee (PPC) provides updates of Grand Challenges (GCs; www.asas.org/about/public-policy/asas-grand-challenges) which clearly articulate research priorities while providing science-based information for shaping public policy, and to enhance future funding for research and education programs in animal sciences (AS). In this nexus symposium for 2021, PPC examines previous stated priorities and provides a progress report card and offers additional perspectives and recommendations for research needed to address some of the GCs continuing to face animal agriculture. Among the GCs is teaching for workforce readiness (WR) as the preparation of graduates for diverse roles in the modern workforce has provoked substantial research in recent years. Ensuring new and returning workplace entrants are prepared to enter the workforce with requisite knowledge, skills, abilities, and experiential attributes required in a diverse array of occupations is a GC. Through an examination of the past, present and future instructional landscape, we will examine a gap which exists between WR and expectations of future employers. Expected employee competencies will include critical thinking and problem solving, mastery of human relationship skills and leadership mindsets, diverse and effective communication abilities, influence skills and workplace emotional intelligence. Instructive to meeting the GC are principles identified in ASAS Accreditation Standards for Animal Science Programs. Those standards strive to ensure graduates can communicate the importance of animal agriculture and STEM biology to an increasingly non-ag world. Among the GC of providing experiences are increased emphasis on high-impact programming around internships, externships and apprenticeships layered onto a knowledge, scaffolded curriculum. Use of research methodology to develop and incorporate into practice varied instructional modalities, technologies and practices within a dynamic higher education landscape requires intentional, systematic, and iterative scholarly approaches; which will enable creation of adaptive learning experiences and learning outcomes needed for an ever-changing workplace with its unique challenges and opportunities.

ARLEAN: An Augmented Reality Learning Analytics Ethical Framework

The emergence of the Learning Analytics (LA) field contextualised the connections in various disciplines and the educational sector, acted as a steppingstone toward the reformation of the educational scenery, thus promoting the importance of providing users with adaptive and personalised learning experiences. At the same time, the use of Augmented Reality (AR) applications in education have been gaining a growing interest across all the educational levels and contexts. However, the efforts to integrate LA techniques in immersive technologies, such as AR, are limited and scarce. This inadequacy is mainly attributed to the difficulties that govern the collection and interpretation of the primary data. To deal with this shortcoming, we present the “Augmented Reality Learning Analytics” (ARLEAN) ethical framework, tailored to the specific characteristics that AR applications have, and focused on various learning subjects. The core of this framework blends the technological, pedagogical, and psychological elements that influence the outcome of educational interventions, with the most widely adopted LA techniques. It provides concrete guidelines to educational technologists and instructional designers on how to integrate LA into their practices to inform their future decisions and thus, support their learners to achieve better results.

Improving Learner-Computer Interaction through Intelligent Learning Material Delivery Using Instructional Design Modeling.

This paper describes an innovative and sophisticated approach for improving learner-computer interaction in the tutoring of Java programming through the delivery of adequate learning material to learners. To achieve this, an instructional theory and intelligent techniques are combined, namely the Component Display Theory along with content-based filtering and multiple-criteria decision analysis, with the intention of providing personalized learning material and thus, improving student interaction. Until now, the majority of the research efforts mainly focus on adapting the presentation of learning material based on students’ characteristics. As such, there is free space for researching issues like delivering the appropriate type of learning material, in order to maintain the pedagogical affordance of the educational software. The blending of instructional design theories and sophisticated techniques can offer a more personalized and adaptive learning experience to learners of computer programming. The paper presents a fully operating intelligent educational software. It merges pedagogical and technological approaches for sophisticated learning material delivery to students. Moreover, it was used by undergraduate university students to learn Java programming for a semester during the COVID-19 lockdown. The findings of the evaluation showed that the presented way for delivering the Java learning material surpassed other approaches incorporating merely instructional models or intelligent tools, in terms of satisfaction and knowledge acquisition.

Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system

Smart learning environments (SLE) provide students with opportunities to interact with learning resources and activities in ways that are customised to their particular learning goals and approaches. A challenge in developing SLEs is providing resources and tasks within a single system that can seamlessly tailor learning experience in terms of time, place, platform, and form. In this paper we introduce the iCollab platform, an adaptive environment where learning activities are moderated through conversation with an intelligent agent who can operate across multiple web-based platforms, integrating formal and informal learning opportunities. Fifty-eight undergraduate computer science students were randomly assigned to either an intervention or control group for the 12 weeks of the pilot study. Learning analytics were used to examine their interactions with iCollab, while their course performance investigated the impact of using iCollab on learning outcomes. Results from the study showed a high level of interaction with iCollab, especially social interaction, indicating an interweaving of formal learning within their informal network spaces. These findings open up new possibilities for ways that SLEs can be designed to incorporate different factors, improving the ability of the system to provide adaptive and personalised learning experiences in relation to context and time.

Adaptive Visual Learning Using Augmented Reality and Machine Learning Techniques

The current curriculum forces students to understand topics by visualizing axonometric structures in their cognitive minds depending upon the conceptual texts and information. This methodology is inconsistent as the idea of visualization through conceptual knowledge is dependent on the level of reasoning and IQ (Intelligence Quotient) a student possesses. It is usually common for a student to misinterpret an information due to lack of reasoning and imaginative skills. Our educational model aims to diminish this intellectual barrier by incorporating Augmented Reality (AR) and Machine Learning (ML) techniques together and create an Adaptive Visual Learning experience for students. A mobile interface with OCR (Optical Character Recognition) and TTS (Text-To-Speech) feature is given to make this whole process simple and easy to use for any student. In this paper, two ML techniques Logistic Regression and Neural Network are applied in order to enhance and modify the existing educational system by removing the intellectual barrier involved due to neurodiversity. A comparative study is performed between the two ML algorithms, where in Logistic Regression performed better than the Neural Network. This form of adaptive visual learning aims to boost student performance in academia.

Learning Analytics in Flipped Classrooms: A Scoping Review

The Flipped Classroom (FC) is an instruction method, where the traditional lecture and homework sessions are inverted. Online material is given to students in order to gain necessary knowledge before class, while class time is devoted to application of this knowledge and reflection. The hypothesis is that there could be deep and creative discussions when teacher and students physically meet, which has known a significant surge of popularity in the past decade. A marked recent trend in the FC is the increased use of Learning Analytics (LA) to support the development of the FC and students’ reflexive learning. The aim of this paper is to investigate the literature on applications of LA in FCs, and to determine the best practices and needs for technological development supporting LA in the FC by means of a scoping review. This literature review revealed that there is potential in using LA in the FC, especially as a means to predict students’ learning outcome and to support adaptive learning and improvement on the curriculum. However, further long‑term studies and development is necessary to encourage self‑directed learning in students and to develop the whole of the FC for a more diverse population of students. We anticipate an increased and expanded use of LA to come, with focus on predictive and prescriptive analytics providing more adaptive learning experience. We also anticipate that LA will expand beyond data mining to correlate student performance and online engagement with the aim to include a wider range of possibilities of interventions and adaptation of the learning experience.

The COVID-19 Pandemic as an Imperative to Advance Medical Student Assessment: Three Areas for Change.

Conditions caused by the COVID-19 pandemic have disrupted clinical practice and all aspects of medical education. Yet the need to continue to train physicians to care for patients and communities is greater than ever. Medical educators are responding by adapting curricula in response to requirements for social distancing, changing availability of clinical learning opportunities, and limitations on testing center availability for examinations. These disruptions require a systems approach to ensure that learners achieve competence in preparation for advancement in training toward unsupervised practice. In this article, the authors assert that medical educators, obligated by current conditions to adapt educational experiences, should seize the opportunity presented by the pandemic to make needed changes in 3 areas aligned with competency-based medical education: focusing on outcomes, broadening the assessment toolbox, and improving the undergraduate medical education (UME)-to-graduate medical education (GME) transition. Defined outcomes, as exemplified by entrustable professional activities, will enable curricular designers to shorten and adapt learning experiences by focusing on students’ achievement of prespecified learning outcomes. Broadening the assessment toolbox entails capturing more and different assessment information about learners to provide a well-rounded view of their strengths and areas for growth in both traditional and novel settings, such as telehealth. Limitations on available data, such as licensing examination scores and clerkship grades, heighten the urgency to revise the system for the UME-to-GME transition by enhancing the quality and usability of information available to residency program directors. Educators should capitalize on the opportunity presented by altered conditions due to the COVID-19 pandemic to make these needed changes to the educational system, to prepare physicians to provide health care and lead the health care system into the future.

Optimal Hierarchical Learning Path Design With Reinforcement Learning.

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners' current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners' hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners' learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.

Covid-19 and rapid adoption and improvisation of online teaching: curating resources for extensive versus intensive online learning experiences

ABSTRACT The COVID-19 pandemic closed university campuses forcing rapid improvisation and adoption of online teaching. This paper explores the experience of converting three modules from proximate to online learning delivery in March and May 2020. This process was facilitated by reflective practice to support a process of improvisation as a buffering response to the pandemic. This paper distinguishes between the development of Distance Learning programmes compared to rapid adoption of online learning. Shifting to complete online teaching involves a process by which the lecturer’s role transitions towards the curation of online and offline student experiences. This includes facilitating and blending extensive and intensive online learning experiences. Extensive involves the selection and curation of online learning support bundles. This requires the creation of learning roadmaps to facilitate student learning. Intensive revolves around online engagement between academics and students and takes two forms: shallow as involving a limited dialogue with students and deep which involves a co-creation process between students and lecturers. Online learning provides opportunities to adapt learning experiences in real-time. The paper evaluates the shift to online practice from the students’ and academics’ perspectives.