Evaluating the performance of students through collaborative learning: Case study: Distance education program in Indonesia

Tjhin, Viany Utami; Rahayu, Anita; Soraya, Kiky

doi:10.1109/hsi.2017.8005006

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…In this perspective, Gokhale (1995) states that Active Methodology gives the student the responsibility to learn and collaborate in the other's learning. For Tjhin et al (2017), it stimulate students to have good management of study time and the ability to self-learn.…”

Section: Active Methodology and Distance Educationmentioning

confidence: 99%

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Andrade¹,

Rigo²,

Barbosa³

2021

Informatics in Education

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Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies' application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students' abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout.

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Section: Active Methodology and Distance Educationmentioning

confidence: 99%

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Andrade¹,

Rigo²,

Barbosa³

2021

Informatics in Education

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“…These educational environments are designed and committed to encouraging both personal and group work in all areas of teaching, as well as to make collaborative learning more efficient, while modern learning has also begun to use AI as a suitable solution. There are currently quite a few collaborative learning systems that are used to solve problems; for example, in English teaching in primary schools, in high school vocational courses, and even in the execution of business operations [27]. However, most of these studies focus on electronic collaborative learning platforms in order to make them a conduit for users to learn online, to have discussions with each other and to access new knowledge.…”

Section: Related Workmentioning

confidence: 99%

ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation

et al. 2020

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Under the vigorous development of global anticipatory computing in recent years, there have been numerous applications of artificial intelligence (AI) in people’s daily lives. Learning analytics of big data can assist students, teachers, and school administrators to gain new knowledge and estimate learning information; in turn, the enhanced education contributes to the rapid development of science and technology. Education is sustainable life learning, as well as the most important promoter of science and technology worldwide. In recent years, a large number of anticipatory computing applications based on AI have promoted the training professional AI talent. As a result, this study aims to design a set of interactive robot-assisted teaching for classroom setting to help students overcoming academic difficulties. Teachers, students, and robots in the classroom can interact with each other through the ARCS motivation model in programming. The proposed method can help students to develop the motivation, relevance, and confidence in learning, thus enhancing their learning effectiveness. The robot, like a teaching assistant, can help students solving problems in the classroom by answering questions and evaluating students’ answers in natural and responsive interactions. The natural interactive responses of the robot are achieved through the use of a database of emotional big data (Google facial expression comparison dataset). The robot is loaded with an emotion recognition system to assess the moods of the students through their expressions and sounds, and then offer corresponding emotional responses. The robot is able to communicate naturally with the students, thereby attracting their attention, triggering their learning motivation, and improving their learning effectiveness.

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“…Tjhin [24] studied the behavior pattern of online learning users in Indonesia, especially that of student users, to find the most suitable and invigorative learning resources for students while improving the difficulty of the learning materials at the same time. Their research data were collected from a private university in Indonesia, 730 student users were selected from 3870 online course users and taken as samples.…”

Section: Introductionmentioning

confidence: 99%

Learners' Autonomous Learning Behavior in Distance Reading Based on Big Data

Shu-liang¹,

Yang²

2022

Int. J. Emerg. Technol. Learn.

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Some learners couldn’t get ideal learning results from distance education, to find out the root of this problem, their learning behavior and initiative should be analyzed in real time. However, in existing research results, the collected distance learning behavior data of learners are not pertinent enough, the extraction methods of big data are not proper enough, and the analysis models of learning behavior are not scientific enough. For these reasons, this paper analyzed the autonomous learning behavior of learners during distance reading based on big data, and gave the analysis contents and methods. In the text, a sequence diagram transformation method that is self-adaptive to long and short sequences had been introduced to process the learning behavior data of learners, who were then classified according to the features of different autonomous learning behavior sequences. Then, an attribute reduction algorithm based on improved Bayesian fuzzy rough set was adopted for attribute reduction, and the behavior indexes that are closely related to the autonomous learning effect of learners were selected for correlation analysis. After that, this paper proposed a method for detecting non-autonomous learning behavior based on multiple time scales, combining short-term autonomous learning pattern with long-term autonomous learning behavior and resource access behavior, this paper also analyzed the typical autonomous learning pattern of learners through the learning of hidden layer features. At last, experimental results proved the effectiveness of the proposed analysis method.

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Evaluating the performance of students through collaborative learning: Case study: Distance education program in Indonesia

Cited by 5 publications

References 4 publications

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation

Learners' Autonomous Learning Behavior in Distance Reading Based on Big Data

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