Personalized Learning Path of a Web-based Learning System

C, Andharini Dwi; Basuki, Ari

doi:10.5120/8434-2206

Cited by 19 publications

(3 citation statements)

References 5 publications

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“…The set criteria are the result of student's individual characteristics, motivation level, fatigue, working environment, learning material (Brusilovsky & Peylo, 2003). The often-used criteria in the research of adaptive learning environments are individual criteria such as:  students' prior knowledge (Dwic & Basuki, 2012) (Sancho, Martinez, & Fernandez-Manjon, 2005) (Klašnja-Milićević, Vesin, Ivanović, & Budimac, 2011), students' learning styles (Hamada, 2012) (Kim, Lee & Ryu, 2013) (Klašnja-Milićević, Vesin, Ivanović, & Budimac, 2011,  students' preferred multimedia for learning (Chorfi & Mohamed, 2004) (Bouzeghoub, Carpentie, Defude, & Duitama, 2003),  students' physical and cognitive characteristics (Yang, Hwang, & Yang, 2013).…”

Section: People: International Journal Of Social Sciences Issn 2454-5899mentioning

confidence: 99%

Smart Classroom Environmental Parameters as a Parameter of Adaptive Learning

Radosavljevic¹,

Radosavljevic²,

Jelić³

2019

pijss

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This paper presents results of the research aimed at establishing the possibility of using a physical environmental parameter (λ) as one of the parameters of adaptive learning in smart classrooms.In this research, the parameter quantifying physical environmental parameters of a smart classroom into a single value was introduced and the relevance of the usage of the introduced parameter as a criterion of adaptive learning in a smart classroom was evaluated. The presentation of multiple environmental parameters through one unique parameter facilitated the realization of adaptation process, especially in the case of applying several adaptation criteria.An overall of 64 third-year students of the ICT College in Belgrade participated in the research.The implemented research drew certain conclusions. The relevance of using the parameter (λ) as the criterion of adaptive learning in smart classrooms was confirmed.

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Section: People: International Journal Of Social Sciences Issn 2454-5899mentioning

confidence: 99%

Smart Classroom Environmental Parameters as a Parameter of Adaptive Learning

Radosavljevic¹,

Radosavljevic²,

Jelić³

2019

pijss

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“…However, it is challenging to promote successful individual learning because of the students' diversities in many aspects such as performance, preference, background knowledge, experience, etc. The diversity of each student creates a sense of awareness of real abilities, skills, and requirements of students (Alian and Jabri, 2009;Graf, 2007) so that the guidance in the right learning pathway can be provided individually (Tan et at, 2012;Dwi and Basuki, 2012).…”

Section: Introductionmentioning

confidence: 99%

Assessment of student competency for personalised online learning using objective distance

Chaichumpa¹,

Temdee²

2018

IJIL

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Like traditional learning, online learning also requires effective personalised learning so that the appropriate feedback can be given individually for the students to achieve their goals. This paper proposes the objective distance which is the measurement representing the distance between current status of student's competency to the satisfied competency level required for accomplishing the entire course. This paper aims to study to what extent the proposed objective distance can be used for effective classification of student's competency comparing to raw score data and its combination. The experiments are conducted with two different online courses including computer skill and English language course having 55 and 111 students respectively. The students are classified with three classifiers including K-nearest neighbour, artificial neural network and decision tree into different classes accordingly to different competency levels. The classification results show that the proposed objective distance can be effectively used for competency classification of online students.

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“…Various approaches to personalize learning path or topic sequencing have been explored in the area of e-learning implementations. Several researches recommended a personalized learning path using feedback, knowledge level and material difficulty [7] while other made used of personalized elearning module through learning style, genetic algorithm and item response theory respectively [8], [9]. The work of [10] on the other hand, manipulated the sequence of problems among learners simultaneously while the works of [11] and [12] were able to sequence several kinds of teaching operations such as presentations, examples and assessments.…”

Section: Personalized Learning Pathmentioning

confidence: 99%

Reversed Roulette Wheel Selection Algorithms (Rwsa) and Reinforcement Learning (Rl) for Personalizing and Improving E-Learning System: The Case Study and Its Implementation

Ballera¹,

Lukandu²

2015

IJEETDM

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Various mechanisms to improve learning process with the objective of maximizing learning and dynamically selecting the best teaching operation to achieve learning goals have been done in the field of personalized learning. However, instructional strategists have failed to address the necessary corrective measures to remediate immediately learning difficulties. It is necessary that an alternative, more realistic, simpler and a real time multi-based performance for personalized learning sequence be developed and implemented. Three major contributions can be asserted by the study: it personalized the learning sequence using reversed roulette wheel selection algorithm and linear ranking; the fitness value is based on real time, multi-based performance system; and it implements the reinforcement and mastery learning to motivate students and to improve their learning output. Result shows that the personalized learning sequence (PLS) were dynamic and heuristic and considers the curriculum difficulty level and the curriculum continuity of successive curriculum while producing individualized and personalized learning sequence. Data collected during 18 weeks experimental sessions, from 34%, a 54% increased has been achieved, making the overall passing rate to 88%. Digital transcripts based on students' perceptions and experiences in using the prototype positively correlates with theme analysis having a score of +.321 with positive attitude such as: very happy, friends, motivate, improve, understanding, knowledge and good were extracted from document analysis.

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Personalized Learning Path of a Web-based Learning System

Abstract: Recently, the growth of web learning system has stimulated the research on adaptive learning opportunity which is suitable to

Cited by 19 publications

References 5 publications

Smart Classroom Environmental Parameters as a Parameter of Adaptive Learning

Smart Classroom Environmental Parameters as a Parameter of Adaptive Learning

Assessment of student competency for personalised online learning using objective distance

Reversed Roulette Wheel Selection Algorithms (Rwsa) and Reinforcement Learning (Rl) for Personalizing and Improving E-Learning System: The Case Study and Its Implementation

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