A framework for an adaptive virtual learning environment

This paper is actually an adapted M.Sc. proposal. Work on this degree has only just start so there are no results to be presented. However, during the CSAW presentation further details will be given about research problems and how they will be solved.peer-reviewe

AdaptiveVLE: an integrated framework for personalised online education using MPS JetBrains domain-specific modelling environment

This paper contains the design and development of an Adaptive Virtual Learning Environment (AdaptiveVLE) framework to assist educators of all disciplines with creating adaptive VLEs tailored to their needs and to contribute towards the creation of a more generic framework for adaptive systems. Fully online education is a major trend in education technology of our times. However, it has been criticised for its lack of personalisation and therefore not adequately addressing individual students’ needs. Adaptivity and intelligence are elements that could substantially improve the student experience and enhance the learning taking place. There are several attempts in academia and in industry to provide adaptive VLEs and therefore personalise educational provision. All these attempts require a multiple-domain (multi-disciplinary) approach from education professionals, software developers, data scientists to cover all aspects of the system. An integrated environment that can be used by all the multiple-domain users mentioned above and will allow for quick experimentation of different approaches is currently missing. Specifically, a transparent approach that will enable the educator to configure the data collected and the way it is processed without any knowledge of software development and/or data science algorithms implementation details is required. In our proposed work, we developed a new language/framework using MPS JetBrains Domain-Specific Language (DSL) development environment to address this problem. Our work consists of the following stages: data collection configuration by the educator, implementation of the adaptive VLE, data processing, adaptation of the learning path. These stages correspond to the adaptivity stages of all adaptive systems such as monitoring, processing and adaptation. The extension of our framework to include other application areas such as business analytics, health analytics, etc. so that it becomes a generic framework for adaptive systems as well as more usability testing for all applications will be part of our future work

Virtual Learning Poltekkes (VILEP) as an Alternative Method for Face-to-face Lectures

Konsep pembelajaran elektronik (e-learning) telah menjadi bagian dari metode pembelajaran baik menjadi metode yang berdiri sendiri maupun menjadi metode campuran yang di padukan dengan metode pembelajaran lainnya. Beberapa penelitian menyebutkan bahwa pembelajaran e-learning efektif dalam meningkatkan hasil belajar. Prodi DIII Kebidanan Poltekkes Kemenkes Palangka Raya menggunakan Virtual Learning Poltekkes Kemenkes (VILEP) yang mana ini merupakan portal layanan e-Learning di Politeknik Kesehatan Kementerian Kesehatan. Evaluasi hasil belajar dengan pembelajaran melalui VILEP ini belum pernah dilakukan. Penelitian ini bertujuan untuk menganalisa efektifitas pembelajaran dengan metode VILEP terhadap hasil belajar mahasiswa Prodi DIII Kebidanan Poltekkes Kemenkes Palangka Raya. Penelitian ini merupakan penelitian quasy exsperiment dengan Nonequivalent Control Group Design. Sampel Penelitian sebanyak 76 responden yang dibagi menjadi 2 kelompok. Analisa data pada penelitian ini  menggunakan  dilakukan dengan Uji Mann Whitney untuk melakukan uji beda pada kedua variabel, serta dilakukan uji regresi linier berganda. Tidak ada beda hasil belajar mahasiswa pada kedua kelompok (p>0,05). Metode VILEP memiliki efektivitas yang sama dengan perkuliahan tatap muka. Metode VILEP dapat digunakan sebagai pengganti saat dosen tidak dapat melaksanakan kuliah tatap muka

Bridging the digital divide for e-learning students through adaptive VLEs

Virtual Learning Environments (VLEs) are required to be highly effective and easy to use as they serve as the primary institutional portal between students and academics. There are currently a number of challenges that are caused due to the modernized digital divide, with a significant limitation being the inability of information systems to adapt to the users' technological platform, broadband quality and device in use to access the online system. This paper focuses on the limitations that students encounter when accessing VLEs within Higher Educational Institutes (HEIs). This research aims to primarily review and provide critical analysis of current VLE frameworks, as well as assess restrictions based on several demographics including content adaptation and technical aspects. An algorithmic system is developed to analyze students' individualistic needs, undertake adaption and personalization of the VLE, and hence ensure consistent and efficient access to academic web resources and functionalitie

AdaptiveSystems: an integrated framework for adaptive systems design and development using MPS JetBrains domain-specific modelling environment

This paper contains the design and development of an adaptive systems (AdaptiveSystems Domain-Specific Language - DSL) framework to assist language developers and data scientists in their attempt to apply Artificial Intelligence (AI) algorithms in several application domains. Big-data processing and AI algorithms are at the heart of autonomics research groups among industry and academia. Major advances in the field have traditionally focused on algorithmic research and increasing the performance of the developed algorithms. However, it has been recently recognized by the AI community that the applicability of these algorithms and their consideration in context is of paramount importance for their adoption. Current approaches to address AI in context lie in two areas: adaptive systems research that mainly focuses on implementing adaptivity mechanisms (technical perspective) and AI in context research that focuses on business aspects (business perspective). There is currently no approach that combines all aspects required from business considerations to appropriate level of abstraction. In this paper, we attempt to address the problem of designing adaptive systems and therefore providing AI in context by utilising DSL technology. We propose a new DSL (AdaptiveSystems) and a methodology to apply this to the creation of a DSL for specific application domains such as AdaptiveVLE (Adaptive Virtual Learning Environment) DSL. The language developer will be able to instantiate the AdaptiveSystems DSL to any application domain by using the guidelines in this paper with an integrated path from design to implementation. The domain expert will then be able to use the developed DSL (e.g. AdaptiveVLE DSL) to design and develop their application. Future work will include extension and experimentation of the applicability of this work to more application domains within British Telecom (BT) and other areas such as health care, finance, etc

AI-Based Adaptive Learning: A Systematic Mapping of the Literature

With the aid of technology advancement, the field of education has seen a noticeable transformation. The teaching-learning process is now more interactive and is no longer restricted to students' physical presence in the classroom but instead makes use of specialized online platforms. In recent years, solutions that offer learning routes customized to learners' needs have become more necessary. In this regard, artificial intelligence has served as an excellent answer, allowing for the building of educational systems that can accommodate a wide range of student needs. Through this paper, a systematic mapping of the literature on AI-based adaptive learning is presented. The examination of 93 articles published between 2000 and 2022 made it possible to draw several conclusions, including the number of adaptive learning environments based on AI, the types of AI algorithms used, the objectives targeted by these systems as well as factors related to adaptation. This study may serve as a springboard for further investigation into how to address the problems raised by the current state.&nbsp

I’ve (Urn)ed This: An Application and Criterion-based Evaluation of the Urnings Algorithm

There is increased interest in personalized learning and making e-learning environments more adaptable. Some e-learning systems may use an Item Response Theory (IRT)-based assessment system. An important distinction between assessment and learning contexts is that learner proficiency is expected to remain constant across an assessment, while it is expected to change over time in a learning context. Constant learner proficiency during an assessment enables conventional approaches to estimating person and item parameters using IRT. These IRT-based systems could be abandoned for alternative approaches to modeling learners and system learning content, but assessments may provide more functions than adapting learning material to students. Thus, there is the question, how can e-learning systems with IRT-based assessment components more dynamically adapt their learning content? Is there a solution that leverages IRT for adapting the learning content of the system? A promising solution is the Urnings algorithm. Like other candidate algorithms, it is computationally light, but this algorithm has mechanisms for preventing variance inflation and is suitable for e-learning contexts. It also provides a measure of uncertainty around estimates. It has been studied both through simulations and applications to e-learning systems. Results are promising; however, there has not been an application of the Urnings algorithm to an e-learning context where there are conventionally estimated person parameters to compare the algorithm estimates to. This study addresses this gap by applying the Urnings algorithm to a K–8 reading and mathematics learning platform. In data from this platform, we have person parameter estimates across academic years from an in-system diagnostic assessment. Results from this study will help industry researchers understand the feasibility of the Urnings algorithm for large e-learning systems with IRT-based assessment components

The Relationship of Four Brain Regions to an Information-Processing Model of Numerical Inductive Reasoning Process: An fMRI Study

The present study relates a four-stage information-processing model of inductive reasoning to four brain regions. We assume that there is a fusiform gyrus region-of-interest (ROI) where a stimulus is visually recognized, a DLPFC ROI where an underlying rule is identified, a caudate ROI where a rule is applied, and a motor ROI where hand movements are programmed during inductive reasoning process. Then, an fMRI experiment was performed to articulate the roles of these four regions. The present study is a 2 (task: rule induction vs. rule application) × 2 (period length: simple vs. complex) × 2 (priming effect: prime vs. target) design. As predicted, both the fusiform gyrus ROI and the motor ROI showed no effects of task, period length, and priming effect, and respectively reflected encoding of stimuli and button-pressing response. The DLPFC ROI responded to task and period length, and was confirmed to play a crucial role in rule identification. The caudate showed no effect of task and responded to period length and priming effect, and was verified to be responsible for rule application. The exploratory analysis also demonstrated our assumptions. Thus, the main stream of information-processing in inductive reasoning process can be described by using the four ROIs

Assessing Adaptive Learning Styles in Computer Science Through a Virtual World

abstract: Programming is quickly becoming as ubiquitous and essential a skill as general mathematics. However, many elementary and high school students are still not aware of what the computer science field entails. To make matters worse, students who are introduced to computer science are frequently being fed only part of what it is about rather than its entire construction. Consequently, they feel out of their depth when they approach college. Research has discovered that by teaching computer science and programming through a problem-driven approach and focusing on a combination of syntax and computational thinking, students can be prepared when entering higher levels of computer science education. This thesis describes the design, development, and early user testing of a theory-based virtual world for computer science instruction called System Dot. System Dot was designed to visually manifest programming instructions into interactable objects, giving players a way to see coding as tangible entities rather than text on a white screen. In order for System Dot to convey the true nature of computer science, a custom predictive recursive descent parser was embedded in the program to validate any user-generated solutions to pre-defined logical platforming puzzles. Steps were taken to adapt the virtual world to player behavior by creating a system to detect their learning style playing the game. Through a dynamic Bayesian network, System Dot aims to classify a player’s learning style based on the Felder-Sylverman Learning Style Model (FSLSM). Testers played through the first half of System Dot, which was enough to test out the Bayesian network and initial learning style classification. This classification was then compared to the assessment by Felder’s Index of Learning Styles Questionnaire (ILSQ). Lastly, this thesis will also discuss ways to use the results from the user testing to implement a personalized feedback system for the virtual world in the future and what has been learned through the learning style method.Dissertation/ThesisMasters Thesis Computer Science 201