An E-Learning Investigation into Learning Style Adaptivity

Abstrac

An E-Learning Investigation into Learning Style Adaptivity.

Abstrac

An E-Learning Investigation into Learning Style Adaptivity.

Abstrac

Guidelines for Effective Adaptive Learning: A Meta-Analysis

Adaptive learning adjusts to the student’s needs to improve learning outcomes, but adaptive learning platforms approach this goal in vastly different ways. When tested, these platforms also show varying levels of success in improving learning. The goal of this meta-analysis is to develop guidelines for the creation and implementation of adaptive learning based on studies where adaptive learning was utilized

MEDIA PEMBELAJARAN PERSONALIZED LEARNING BERBASIS WEB UNTUK MENINGKATKAN KEMAMPUAN KOGNITIF SISWA SMK PADA MATA PELAJARAN PEMROGRAMAN DASAR

Pemrograman Dasar merupakan mata pelajaran yang dianggap sulit karena berisi konsep abstrak, faktor yang menyebabkan kesulitan siswa adalah metode pembelajaran. Dalam pembelajaran karakteristik individu yang berbeda-beda adalah faktor yang mempengaruhi pembelajaran, sehingga diperlukan metode pembelajaran yang berpusat pada siswa. Solusi yang diajukan untuk menyelesaikan masalah tersebut adalah media pembelajaran berbasis web dengan pendekatan personalized learning. Penelitian ini bertujuan untuk mengetahui peningkatan kemampuan kognitif siswa pada mata pelajaran pemrograman dasar setelah menggunakan media pembelajaran berbasis web dengan pendekatan personalized learning. Penelitian ini menggunakan metode penelitian kuantitatif kausal komparatif, pengembangan media yang digunakan adalah Metode Siklus Hidup Menyeluruh. Hasil yang didapatkan dari penelitian ini, media pembelajaran berbasis web yang telah dikembangan mendapatkan rata-rata persentase sebesar 83,98% dari ahli dengan kategori “Sangat Baik”. Media pembelajaran berbasis web terbukti membantu meningkatkan kemampuan kognitif siswa, diperoleh rata-rata nilai gain 0,47 pada kelompok yang dibebaskan untuk mengakses materi, dan rata-rata gain 0,53 pada kelompok yang belajar sesuai dengan gaya belajarnya, keduanya berada pada kriteria “Sedang”. Berdasarkan analisis korelasi, terdapat hubungan antara penilaian siswa terhadap media pembelajaran dengan peningkatan n-gain. Peserta didik memberikan tanggapan terhadap media, didapatkan rata-rata presentase sebesar 94,43%. Basic programming is a difficult lesson because it contains abstract concepts, the factor that causes student difficulties is the learning method. In the learning process, different individual characteristics are factors that influence learning, so a student-centered learning method is needed. The solution proposed to solve this problem is a web-based learning media with a personalized learning approach. This study aims to see the increase in students' cognitive abilities in basic programming subjects after using web-based learning media with a personalized learning approach. This research uses comparative causal quantitative research methods, the media development uses SHM (Siklus Hidup Menyeluruh) method. The results obtained from this research is, the web-based learning media that has been developed get an average percentage of 83.98% from the experts in the "Very Good" category. Web-based learning media is proven to help improve students' cognitive abilities, the average gain value of 0.47 is obtained in the group that is freed to access the material, and an average gain of 0.53 in the group that is learning according to their learning style, both are in the criteria "Medium”. Based on the results of the correlation analysis, there is a relationship between students' assessment of the learning media used and the increase in n-gain. Students gave responses to the media, the average percentage was 94.43%

A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education

Adaptation based on learning style and knowledge level in e-learning systems

Although there have been numerous attempts to build and evaluate adaptive e-learning systems, they tend to be limited in scope, and suffer from a lack of carefully designed and controlled experimental evaluations of their effectiveness and usability. This thesis addresses these issues through the implementation of an adaptive e-learning system and its experimental validation. The design of an adaptive framework and the specific instantiation of its components into a configurable adaptive e-learning system are presented. The domain model of the system deals with computer security. The learner model incorporates the information perception dimension of the Felder-Silverman model of learning style and also knowledge level. The adaptation model generates personalised learning paths and offers adaptive guidance and recommendation. The thesis also provides an empirical evaluation through three controlled experiments to investigate the effect of different forms of adaptation. Rigorous experimental design, careful investigation and precise reporting of results are taken into account in all the three experiments. The findings indicate that matching the sequence of learning objects to the information perception learning style yields significantly better learning outcome and learner satisfaction than non-matching sequences. They also indicate that adaptation based on the combination of the information perception learning style and knowledge level yields significantly better learning outcome (both in the short- and long-term) and learner satisfaction than adaptation based on either of these learner characteristics alone; this combination is also marked by a significantly higher level of perceived usability compared to a non-adaptive version of the e-learning system