scholarly journals Adaptive Learning by Using SCOs Metadata

10.28945/3138 ◽  
2007 ◽  
Author(s):  
Danijela Milosevic ◽  
Mirjana Brkovic ◽  
Matjaz Debevc ◽  
Radojka Krneta
Keyword(s):  
Learning Style ◽  
Adaptive Learning ◽  
Instructional Material ◽  
Learner Characteristics ◽  
Instructional Content ◽  
Lesson Content ◽  
Motivation Level ◽  
Student Information ◽  
Future Work ◽  
Educational Category

This paper presents an adaptation scenario for tailoring instructional content towards individual learner characteristics taking into consideration his/her learning style type and subject matter motivation level. Learning resources are organized through shareable content objects (SCOs) - a small digital chunks of knowledge, independent and self described pieces of instructional material delivered via Learning Management System (LMS). We use an ontology based student model for storing student information. The scenario of designing lesson content is presented as a cross section of learning style and motivation level, based on the learning object’s educational metadata. Adaptation is made through discovering those SCO’s whose educational category metadata implies that SCO is to be delivered for the learning style of user. Our future work will be to provide experiment and to test our proposed guidelines in order to get feedback on how learners see the adaptive learning environments tailored to their individual learning style and motivation characteristics.

scholarly journals MILO - A Proposal of Multiple Intelligences Learning Objects

10.28945/3137 ◽  
2007 ◽  
Author(s):  
Hamilton Matos ◽  
Pollyana Mustaro ◽  
Ismar Silveira
Keyword(s):  
Instructional Design ◽  
Multiple Intelligences ◽  
Learning Style ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Learning Object ◽  
Proof Of Concept ◽  
Personal Learning ◽  
Learning Contexts ◽  
Instructional Content

Learning objects-driven approaches for the development of instructional content have been widely used to structure entire courses and repositories for distinct learning contexts. Nonetheless, their use is still done in a static, non-adaptive manner, since students are presented to prebuilt compilations of learning object having few or none relationship with its learning current conditions, history or personal learning style, which together compose the student momentum. This work presents an analysis of current instructional design and multiple intelligences theories in order to create learning objects that provide adaptive learning methods according to different students’ characteristics. Using technologies that allow such dynamic approaches, it was created -as a proof of concept - a learning object about the Pythagorean Theorem.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

Why Students Should Be Taught Differently: Learner Characteristics, Learning Styles and Simulation Performance

2021 ◽  
pp. 104687812110658
Author(s):  
Bindu Kulkarni ◽  
Ranjan Banerjee ◽  
Rajasekaran Raghunathan
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Reflective Learning ◽  
Outcome Variable ◽  
Full Time ◽  
Business Simulation ◽  
Older Students ◽  
Learner Characteristics ◽  
Simulation Performance ◽  
Business Simulations

Background Business simulation as an instructional tool helps in developing integrative thinking and decision making skills. It is being taught to audiences who differ considerably in age, work experience (learner characteristics) and learning styles. The use of simulations is likely to grow further with advancements in internet technology and the fact that simulations are very amenable to remote modes of instruction. Aim This study aims to assess how learner characteristics and learning styles impact business simulation performance. It further assesses the combined effect of learner characteristics and learning styles on performance in business simulations, we specifically consider the manner in which learning styles moderate the impact of learner characteristics (age) on simulation performance. Method The study was conducted with 605 students of full time MBA and executive MBA programs with age group varying from 21 years to 53 years. They were taught using the same business simulation by CAPSIM. The learning styles were measured using Felder-Solomon’s instrument ‘Index of learning style’. Regression analysis was conducted with predictor variables of learner characteristics and learning styles and outcome variable of simulation performance. The moderating effect of specific learning styles on learner characteristics was identified. Results The findings indicate that age is a significant predictor of simulation performance (younger, tech savvy students do better). Also, the use of reflective learning style enables better performance in business simulations. Older students are able to draw on experience and benefit more from reflective learning, for business simulations which involve integration across functions. Conclusion The study enhances our conceptual understanding of the factors enabling performance in business simulations and provides specific direction on how instructors must adapt facilitation approaches for different age groups of participants. Reflection is important for learning with business simulations; hence, the reflective learning style should be encouraged particularly among older students.

scholarly journals Learning Trigonometry Using GeoGebra Learning Module: Are Under Achiever Pupils Motivated?

2017 ◽  
Vol 9 (1-2) ◽  
Author(s):  
Muhammad Hafizhuddin Abdul Rahman ◽  
Marzita Puteh
Keyword(s):  
Teaching And Learning ◽  
Instructional Material ◽  
Learning Module ◽  
Learning Mathematics ◽  
Explanatory Mixed Methods ◽  
Motivation Level ◽  
Research Questions ◽  
Dynamic Software ◽  
Level Of Motivation ◽  
Learning Concept

Under achiever pupils were found to have low motivation level in learning mathematics. Pupils avoid Trigonometry and consider this topic as tough for them to master. The objective of this research is to measure under achiever motivation level towards Teaching and Learning (T&L) Trigonometry II topic using GeoGebra Learning Module (GLM). This research has used explanatory mixed methods which is survey and interview to answer the research questions. Pupils were exposed to GLM in Trigonometry II topics for a duration of two weeks. Motivation level are measured using questionnaire adapted from Instructional Material Motivational Scale (IMMS). Five pupils then were interviewed by the researcher to gauge pupils motivation towards GLM. Respondents are from 21 under achiever pupils from one intact class in Muar, Johor. Data analysis showed overall pupils’ motivation level are high (M = 4.16, SD = .279). The level of motivation of pupils for each subscale in ascending order is the Confidence (M = 3.92, SD = .268), Relevant (M = 4.07, SD = .357), Attention (M = 4.25, SD = .320), and Satisfaction (M = 4.50, SD = .436). Girls are significantly motivated [t (19) = 2,401, p <0.05, r = 1.07] compared to boys in using GLM. Result from the interviews were found similar to the surveys result. Pupils were found to be attracted to self access learning concept and slider function in the learning process. It is recommended that mathematics teachers should use GeoGebra by developing learning module that combine mathematics dynamic software and written module to help under achiever pupils further improve their motivation in T&L.  

scholarly journals Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

2021 ◽  
Vol 4 (2) ◽  
pp. 55-76
Author(s):  
Dan Oyuga Anne ◽  
Elizaphan Maina
Keyword(s):  
Learning Style ◽  
Affective State ◽  
Learner Characteristics ◽  
Q Learning ◽  
Model Free ◽  
Learning Platform ◽  
Learning Path ◽  
Multiple Characteristics ◽  
E Learning ◽  
Extraction Model

We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

Development of Adaptive Kanji Learning System for Mobile Phone

2012 ◽  
pp. 181-192
Author(s):  
Mengmeng Li ◽  
Hiroaki Ogata ◽  
Bin Hou ◽  
Satoshi Hashimoto ◽  
Yuqin Liu ◽  
...  
Keyword(s):  
Language Learning ◽  
Mobile Phone ◽  
Learning Style ◽  
Adaptive Learning ◽  
Learning Systems ◽  
Learning System ◽  
Difficulty Level ◽  
Proficiency Level ◽  
Main Emphasis ◽  
Adaptive Learning System

This paper describes an adaptive learning system based on mobile phone email to support the study of Japanese Kanji. In this study, the main emphasis is on using the adaptive learning to resolve one common problem of the mobile-based email or SMS language learning systems. To achieve this goal, the authors main efforts focus on three aspects: sending the contents to a learner following his or her interests, adjusting the difficulty level of the tests to suit the learner’s proficiency level, and adapting the system to his or her learning style. Additionally, this system has already been evaluated by the learners and the results show that most of them benefited from the system and would like to continue using it.

The Learning Style-Based Adaptive Learning System Architecture

2015 ◽  
Vol 5 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Chyun-Chyi Chen ◽  
Po-Sheng Chiu ◽  
Yueh-Min Huang
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
System Architecture ◽  
Learning System ◽  
Learning Materials ◽  
Learning Path ◽  
Different Types ◽  
Learning System Architecture ◽  
Adaptive Learning System

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

Variant Texts According to Types of Sensory Perception

2017 ◽  
Vol 6 (4) ◽  
pp. 36-47
Author(s):  
Milan Pastyřík ◽  
Petr Škuta ◽  
Ondřej Takács ◽  
Aleš Oujezdský
Keyword(s):  
High School ◽  
High School Students ◽  
Learning Style ◽  
Information And Communication Technologies ◽  
Adaptive Learning ◽  
Sensory Perception ◽  
Communication Technologies ◽  
Thinking Styles ◽  
School Students ◽  
Information And Communication

AbstractThe paper deals with a problematic of creating variant texts according to a sensory perception. An idea of transcribing text is based on a theory of adaptive learning, which is thoroughly studied at the Department of Information and Communication Technologies. Researchers in this work combined the adaptive approach together with thinking styles introduced by Libor Činka and created four variants of texts of the chosen topics. Then those texts undergone the verification by the students from high school and university, who read them and evaluated them as well as they answered to a prepared set of testing questions. All received data was compared against the replies from the learning style questionnaires VARK and questionnaire by Šimíčková. The paper discovered some differences between the results of VARK and Šimíčková questionnaire, which proved to be slightly more reliable compared to both the results of test questions and the students’ own opinion. There were also differences between sensory variants of texts. As expected, the kinesthetic variant proved to be the less effective compared to the rest. It seems that university students accepted the rewritten texts better than high school students too.

scholarly journals SCIENCE EDUCATION ADAPTIVE LEARNING SYSTEM AS A COMPUTER-BASED SCIENCE LEARNING WITH LEARNING STYLE VARIATIONS

2018 ◽  
Vol 17 (4) ◽  
pp. 711-727 ◽  
Author(s):  
Zulfiani Zulfiani ◽  
Iwan Permana Suwarna ◽  
Sujiyo Miranto
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
Science Learning ◽  
Learning System ◽  
Learning Material ◽  
Learning Approaches ◽  
Slow Learners ◽  
Computer Based ◽  
Adaptive Learning System

Students with their different learning styles also have their own different learning approaches, and teachers cannot simultaneously facilitate them all. Teachers’ limitation in serving all students’ learning styles can be anticipated by the use of computer-based instructions. This research aims to develop ScEd-Adaptive Learning System (ScEd-ASL) as a computer-based science learning media by accommodating students’ learning style variations. The research method used is a mixed method at junior high schools in Tangerang Selatan. The final product of the research is a special learning media appropriate to students’ visual, aural, read/write and kinesthetic learning styles. The uniqueness of the media is its form of integrated science materials, accommodating fast and slow learners, and appropriate to their learning styles. ScEd-Adaptive Learning System as a developed computer-based science learning media was declared as good and valid by four media experts and five learning material experts. ScEd-ALS for kinesthetic style has a high effectivity to improve students learning mastery (100%), consecutively aural (63%), read/write (55%), and visual (20%). This media development can be continued with the Android version or iOS to make it more operationally practical. Keywords: adaptive learning system, science learning media, computer-based instruction, learning style.

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