• Educational Technology International
• /
• v.20 no.2
• /
• pp.223-255
• /
• 2019

The purpose of this study is to draw implications for designing online tools to support monitoring in collaborative learning. For this purpose, eighteen research papers that explored learner dashboards and group awareness tools were analyzed. The driving questions for this analysis related to the information and outcomes that must be monitored, whose performance they represent, and who monitors the extent of learning. The analytical frameworks used for this study included the following: three modes of co-regulation in terms of who regulates whose learning (self-regulation in collaborative learning, other regulation, and socially shared regulation) and four categories of dashboard information to determine which information is monitored (information about preparation, participation, interaction, and achievements). As a result, five design implications for learner dashboards that support monitoring were posited: a) Monitoring tools for collaborative learning should support multiple targets: the individual learner, peers, and the entire group; b) When supporting personal monitoring, information about the individual and peers should be displayed simultaneously to allow direct comparison; c) Information on collaborative learning achievements should be provided in terms of the content of knowledge acquired rather than test scores; d) In addition to information related to interaction between learners, the interaction between learners and learning materials can also be provided; and e) Presentation of the same information to individuals or groups should be variable.

• Educational Technology International
• /
• v.22 no.2
• /
• pp.255-283
• /
• 2021

In the early 2020, COVID-19 changed the traditional way of teaching and learning. This paper aimed to explore the impact of college students' perception of course quality on their online learning satisfaction. A total of 4,812 valid samples were extracted, and the difference analysis and hierarchical regression analysis were used to make an empirical analysis of college students' online learning satisfaction. The research results were as follows. Firstly, there was no difference in online learning satisfaction among students by gender and grade. Secondly, learning assessment, course materials, course activities and learner interaction, and course production had a significant positive impact on online learning satisfaction. Course overview and course objectives had an insignificant correlation with online learning satisfaction. Thirdly, the total effect of online learning satisfaction was as follows. Course production had the greatest effect, followed by course activities and student-student interactions, followed by course materials. It was the learning evaluation that showed the least effect. This study can provide empirical reference for college teachers on how to continuously improve online teaching and increase students' satisfaction with online learning.

• Journal of Practical Engineering Education
• /
• v.13 no.2
• /
• pp.333-349
• /
• 2021

The fourth industrial revolution based on information and communication technology has increased the need for an environment where contents in new technologies can be learned for the development of lifelong vocational capabilities. To prepare for this, K University's online lifelong education center has established STEP, a smart learning platform. In this study, we conducted a study and other platform case analysis for STEP learner types, a survey of learners, and a comprehensive analysis based on these results to classify characteristics by learner types. It also intended to establish a plan to provide customized services to meet the needs of STEP learners in the future. The derived results are as follows. It is necessary to constantly manage learning content difficulty and learning motivation survey, and also needs to refine the operation of learning content in terms of learning composition. In addition, it is important to secure specialized content, to manage vulnerable learners, to actively introduce a learner support system and various educational methods.

• Journal of Digital Contents Society
• /
• v.15 no.2
• /
• pp.155-165
• /
• 2014

This study deals with the proposals and the verification of the causal relationship between improvements of corporate manager's learning attitude for enterprises productivity and managerial effectiveness, the learner's familiarity and personal character strengths that are classified as positive psychology-based theory. For this study, researchers designed the structural equation model using the AMOS analysis. And researchers conducted the interviews using a questionnaire survey of quantitative research methods. Results of the analysis are as below. First, both online and offline classes were identified as the learner's character strengths affect the positive effect to the learner's familiarity strengths. Second, the learner's familiarity affects the positive effect to improvements of learning attitude. And the last, the learner's character strengths and improvements of learning attitude are not significant effect each other, therefore researcher could be found that the role of mediating variables of the learner's familiarity strengths. In this study, researchers analyzed the relationship between familiarity strengths and improving the learning attitudes needed for a desirable curriculum development considering the learners' personality factors. These findings contribute to an ideal course of study when educator design a curriculum and contribute to a successful communication strategy.

• Journal of Information Technology Services
• /
• v.20 no.4
• /
• pp.129-140
• /
• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.

• International journal of advanced smart convergence
• /
• v.8 no.2
• /
• pp.8-17
• /
• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• The Journal of the Korea Contents Association
• /
• v.19 no.10
• /
• pp.385-396
• /
• 2019

This study was conducted to explore the characteristics of learner who showed free riding behavior in online collaborative learning. For this, 290 students from three universities in the metropolitan area were studied. The collected data are as follows. Learner characteristics are learning strategy, learning motivation, academic retardation behavior, and learning disposition. Interaction distinguished between frequency and type of message. Interaction levels were collected with frequency. The subjects with less than 5 interaction frequencies were defined as free-riding students. 43 students were classified as free riders. Learner characteristics were analyzed by cluster analysis. As a result, the learner characteristics were divided into five groups. All the free riding students belonged to 4 groups. The learner characteristics of 4 groups are as follows. First, the level of the learning strategy is very low. Second, learning motivation has a high tendency toward performance - oriented approach and high tendency to avoid performance. This tends to deliberately avoid learning. Third, the level of delayed behavior is high. This is deliberately putting off student activities. Fourth, learning tendency is high in academic anxiety, task value, self efficacy and learning belief are very low. This is a lack of confidence in learning.

• Educational Technology International
• /
• v.10 no.1
• /
• pp.59-78
• /
• 2009

Language Capital is one of the most important academic abilities and competencies for new era of globalization worldwide. In Europe and Asia where English is not the native language, it is necessary to encourage higher education to improve language competency from both qualitative and quantitative aspects. In so far as online university concerned, it appears of significance to prepare for globalization from the perspective of cross border education, and it needs to focus on how to design and develop English-medium teaching (EMT) or in other words English mediated instruction(EMI) for both teachers and students. In order to provide supportive programs of English-mediated class for teachers and students, the study examined and analyzed what abilities are needed for teachers based on DACUUM approach, suggesting teachers' competency as well as strategies for online- EMT. Based on literature review, DACUUM analysis, focus group interview with teachers and students who experienced online EMT, online programs supporting both teachers and students for online EMI were developed. This program expects to play roles of practical guidelines and reference for both teachers and students online in an extension of language capital improvement.

• English Language & Literature Teaching
• /
• v.16 no.4
• /
• pp.39-69
• /
• 2010

This study aims to identify how participants in online-incorporated English learning perceive interaction between achievement and factors of learning and personality. Using grounded theory analysis, this study attempts to generate a theoretical model depicting how the factors work with the L2 learners situated in the learning setting. A total of 231 college freshmen participated in online and offline EFL learning programs for the duration of one semester. In addition, all respondents completed a survey questionnaire on their learning experiences. In the investigation of the differences between low- and high-proficiency groups, audio-taped interviews with 20 selected students, 10 from each group, have revealed differences not only in the types of personal and instructional factors, but also, more importantly, in the interrelationship between these factors in each group's learning model. These models effectively explained the statistically significant differences in four questionnaire items, such as online learning and contributions of offline class sections to their L2 achievement. These findings entail L2 practitioners' shared understandings of their students' perspectives of learning in the specific L2 learning context.

• Journal of Internet Computing and Services
• /
• v.20 no.5
• /
• pp.79-86
• /
• 2019

This paper is an overview of key elements of Korea National Open University's smart mobile learning system, and an attempt to evaluate its main services relative to the FRAME model and the Mobile Learning Development Model for distance learning in higher education. KNOU improved its system architecture to one based on xMOOC e-learning content delivery while also upgrading its PC-based online/mobile learning services to facilitate an easier and more convenient access to lectures and for better interactivity. From the users' viewpoint, the upgraded 'U-KNOU Campus' allows for a more integrated search capability coupled with better course recommendations and a customized notification service. Using the new system, the students can access not only the school- and peer-issued messages via online bulletin boards but also share information and pose questions to others including to the school faculty/officials and system administrators. Additionally, a new mobile payment method has been incorporated into the system so that the students can select and pay for additional courses from anywhere. In spite of these advances, the issue of device usability and content development remain; specifically U-KNOU Campus needs to improve its instructor-learner and learner-to-learner interactivity and mobile evaluation interface.