Student Engagement with Online Tutorial: A Perspective on Flow Theory

Santosa, Paulus Insap

doi:10.3991/ijet.v10i1.4348

Cited by 19 publications

(11 citation statements)

References 34 publications

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“…Engagement consists of users, activities, attitudes, goals and mental models, and motor skills and can be shown in the form of attention, intrinsic interest, curiosity and motivation [17]- [20]. Student engagement generally described as a student actively engages with the content provided in the course in the way of thinking, talking and interacting with [21].…”

Section: Student Engagementmentioning

confidence: 99%

Conceptual Model for Profiling Student Behavior Experience in e-Learning

Man

Azhan

Hamzah

2019

Int. J. Emerg. Technol. Learn.

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Most e-Learning web application known as Learning Management Systems are associated with collaboration in a web page. It allows a user to interact directly with multiple application in any web platform together with other users. However, the action of the users has not been thoroughly analyzed. Due to the medium of teaching, implementation is through online. It is nec-essary to analyse each student behaviour characteristics of blended learning implementation so that lecturer can adjust how online activities are per-formed. In this paper, we propose a conceptual model in profiling student behaviour in e-Learning based on metadata approach and Community of In-quiry Model. We adopt a metadata approach in collecting student experience in e-Learning and Community of Inquiry Model to mapping the online stu-dent experiences. This conceptual model provides the basis for evaluating student behaviour characteristics in online learning with the goal of im-proved student engagement and online activity design.

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Section: Student Engagementmentioning

confidence: 99%

Conceptual Model for Profiling Student Behavior Experience in e-Learning

Man

Azhan

Hamzah

2019

Int. J. Emerg. Technol. Learn.

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“…Current learning models have undergone changes from conventional learning to online learning models; these models are also known as e-learning, online learning, distance learning or web-based tutorials (Santosa, 2015). E-learning is used not only as a complement to learning but can also enrich conventional learning (Nugroho, 2013).…”

Section: Introductionmentioning

confidence: 99%

Model detecting learning styles with artificial neural network

Hasibuan¹,

Nugroho²,

Santosa³

2019

J. Technol. Sci. Educ.

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Currently the detection of learning styles from the external aspect has not produced optimal results. This research tries to solve the problem by using an internal approach. The internal approach is one that derives from the personality of the learner. One of the personality traits that each learner possesses is prior knowledge. This research starts with the prior knowledge generation process using the Latent Semantic Indexing (LSI) method. LSI is a technique using Singular Value Decomposition (SVD) to find meaning in a sentence. LSI works to generate the prior knowledge of each learner. After the prior knowledge is raised, then one can predict learning style using the artificial neural network (ANN) method. The results of this study are more accurate than the results of detection conducted with an external approach.

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“…In addition to the learning model, priorknowledge can influence learning outcomes (Santosa, 2015 andErtl &Mandl, 2008). According to Karunia and Yudhanegara (2015), learning can work well if one can adapt new knowledge to the cognitive structure it has.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Mathematical Reflective Thinking Skills Based on Learning Model And Mathematical Prior Knowledge

Muin

Novianti²,

Musyrifah

2018

Proceedings of the International Conference on Education in Muslim Society (ICEMS 2017)

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In several types of research, analysis about mathematical reflective thinking skills viewed based on one independent variable as treatment given. The purpose of the research was to analyze the mathematical reflective thinking skills not only based on learning model but also viewed based on mathematical priorknowledge. The research was conducted at one of state junior high schools in Tangerang Selatan on the academic year 2017. The sample was taken through cluster random sampling technique as many as two classes with the total amount about 72 students. As a treatment, ill-structure problem-solving learning model implemented on experimental class and conventional learning models implemented on control class. Mathematical prior-knowledge was distinguished into three levels: low, middle, and high. Mathematical reflective thinking skills viewed based on three indicators: identifying a problem situation, predicting a solution, and evaluating the situation. Two Way Analysis of Varian has used to analyze the data with the level of significance about 0.05. The results showed that: 1) there was an effect of learning model towards students mathematical reflective thinking skills overall, 2) there was an effect of mathematical prior-knowledge towards students mathematical reflective thinking skills overall, and 3) there was interactional effect between learning model and mathematical prior-knowledge towards students mathematical reflective thinking skills. In general, the results showed that ill-structured problem-solving learning model contribute to students mathematical reflective thinking skills with a high and middle level of prior-knowledge.

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Student Engagement with Online Tutorial: A Perspective on Flow Theory

Cited by 19 publications

References 34 publications

Conceptual Model for Profiling Student Behavior Experience in e-Learning

Conceptual Model for Profiling Student Behavior Experience in e-Learning

Model detecting learning styles with artificial neural network

Analysis of Mathematical Reflective Thinking Skills Based on Learning Model And Mathematical Prior Knowledge

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