• Educational Technology International
• /
• v.11 no.2
• /
• pp.43-75
• /
• 2010

The purpose of this study is to identify patterns of learner behaviors, conflicting and facilitating factors during collaborative work in an online learning community(OLC). This study further seeks to investigate the difference of learner behaviors between high- and low-performing groups, and conflicting and facilitating factors. The online postings from four groups(19 students) in the spring semester(study 1) and six groups(24 students) in the fall semester(study 2) were analyzed. A coding scheme was generated based on constant comparison using the qualitative data analysis tool, NVivo. The analysis identified 7 categories of learner behaviors in both studies. Among the seven categories, information seeking and co-construction were most frequently observed in both studies. One evident difference between the high- and low-performing groups was that the high-performing groups revealed more incidents of learner behaviors in both studies. In addition, six categories of conflicting factors and five categories of facilitating factors were emerged in both studies. The inefficiency of work category was one of the most frequently observed categories in both studies. Interestingly, the high-performing groups showed more incidents of conflicting factors than the low-performing groups. This study revealed two different types of conflicting factors and there is a need for different moderating strategies depending on its type. Based on the results of the study, effective design strategies for an OLC to facilitate active learning were suggested.

• Korean Journal of Community Nutrition
• /
• v.9 no.4
• /
• pp.464-471
• /
• 2004

For the purpose of developing ‘Web-Based Nutritional Education Program’, this study did instructional analysis, analysis of entry behaviors and learner characteristics to select instructional contents for nutritional education program for junior/senior high school students. Survey questionnaires were distributed from October 21, 2002 to October 26, 2002 to a total of 564 junior/senior high school students in Seoul. The total number of questionnaires collected and used in our study was 479, which is 84.9% of the total questionnaires distributed. As a result of the curriculum analysis of ‘Technology-Home economics’, subjects related with ‘food and nutrition’ in the 7$^{th}$ educational course study result, lessons including the contents of food and nutrition were ‘nutrition and meal of adolescent’ in 7th grade, ‘family meal management’ in 9th grade and ‘practice of home life’ in 10th grade. According to the curriculum analysis, ‘nutrition’ and ‘food’ were chosen for the superior subjects of nutrition education program and four subordinate subjects per superior one were developed. Then, entry behaviors and learner characteristics were analyzed： life characteristics, internet usage, anthropometrics data and nutritional knowledge. Students were interested in using computer and preferred passive activity to dynamic activity. According. to the analysis of internet usage, WBI for students should composed active parts such as game, moving image, immediate reply and follow-up bye-mail, bulletin board and com- munity activity. Students’ BMI were in normal range but they were lacked nutritional knowledge. We wrote performance objectives that were specific behavior skills to be learned, the conditions under which they must be performed and the criteria for successful performance. The next step for developing the WBI nutrition education program would be based on the results mentioned above.

• Asia Pacific Journal of Corpus Research
• /
• v.5 no.1
• /
• pp.23-36
• /
• 2024

This research centers on the Taiwan Eye-Movement Corpus of Spanish (TECS), a specially created corpus comprising eye-tracking data from Chinese-speaking learners of Spanish as a third language in Taiwan. Its primary purpose is to explore the broad utility of TECS in understanding language learning processes, particularly the initial stages of language learning. Constructing this corpus involves gathering data on eye-tracking, reading comprehension, and language proficiency to develop a machine-learning model that predicts learner behaviors, and subsequently undergoes a predictability test for validation. The focus is on examining attention in input processing and their relationship to language learning outcomes. The TECS eye-tracking data consists of indicators derived from eye movement recordings while reading Spanish sentences with temporal references. These indicators are obtained from eye movement experiments focusing on tense verbal inflections and temporal adverbs. Chinese expresses tense using aspect markers, lexical references, and contextual cues, differing significantly from inflectional languages like Spanish. Chinese-speaking learners of Spanish face particular challenges in learning verbal morphology and tenses. The data from eye movement experiments were structured into feature vectors, with learner behaviors serving as class labels. After categorizing the collected data, we used two types of machine learning methods for classification and regression: Random Forests and the k-nearest neighbors algorithm (KNN). By leveraging these algorithms, we predicted learner behaviors and conducted performance evaluations to enhance our understanding of the nexus between learner behaviors and language learning process. Future research may further enrich TECS by gathering data from subsequent eye-movement experiments, specifically targeting various Spanish tenses and temporal lexical references during text reading. These endeavors promise to broaden and refine the corpus, advancing our understanding of language processing.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.11
• /
• pp.3241-3245
• /
• 2009

In this paper we propose mechanism for course scheduling of learner-oriented using weakness analysis algorithm. The proposed mechanism monitors learner's behaviors constantly evaluates them and calculates his accomplishment. From this accomplishment the schedules the suitable course for the learner. The learner achieves an active and complete learning from the repeated and suitable course.

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.519-525
• /
• 2006

Advances in information and telecommunication technology increasingly reveal the potential of computer supported education. However, most computer supported learning systems until recently did not pay much attention to different characteristics of individual learners. Intelligent learning environments adaptive to learner's preferences and tasks are desired. Each learner has different preferences and needs, so it is very crucial to provide the different styles of learners with different learning environments that are more preferred and more efficient to them. This paper reports a study of the intelligent learning environment where the learner's preferences are diagnosed using learner models, and then user interfaces are customized in an adaptive manner to accommodate the preferences. In this research, the learning user interfaces were designed based on a learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. Then, a learning style modeling is done from learner behavior patterns using Decision Tree and Neural Network approaches. In this way, an intelligent learning system adaptive to learning styles can be built. Further research efforts are being made to accommodate various other kinds of learner characteristics such as emotion and motivation as well as learning mastery in providing adaptive learning support.

• The Journal of Korean Association of Computer Education
• /
• v.23 no.2
• /
• pp.53-64
• /
• 2020

As online learning forms centered on video lectures become more common and constantly increasing, the video-based learning environment applying various educational methods is also changing and developing to enhance learning effectiveness. Learner's log data has emerged for measuring the effectiveness of education in the online learning environment, and various analysis methods of log data are important for learner's customized learning prescriptions. To this end, the study analyzed learner behavior data and predictions of achievement by machine learning in video-based learning environments. As a result, interactive behaviors such as video navigation and comment writing, and learner-led learning behaviors predicted achievement in common in each model. Based on the results, the study provided implications for the design of the video learning environment.

• 한국HCI학회:학술대회논문집
• /
• 2008.02b
• /
• pp.71-77
• /
• 2008

This study conducted to tried to find a way of on-line assessment of learner's interest and comprehension during interactive learning process. The result of experiment confirmed hat learners' behavior patterns acquired from log data could be good predictors of learner's level of interest and comprehension in actual performance on KORI program. To predict learning outcome depending on the behaviors of individual learners, self-efficacy and mastery goal orientation were measured as individual differences. Then, participants were asked to use TA program KORI program at home for ten days and their response patterns were recorded through network. After using KORI, the levels of interest and comprehension were measured. As the result of multiple regression analysis, each learner's interest and comprehension were predicted depending on the combination of log data captured on real-time. This prediction process was done differently depending on learners' characteristics. Since equations which predict learners' interest and comprehension are different depending on learners' characteristics, differential interfaces should be provided depending upon changes in their level of interest and comprehension.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.2799-2823
• /
• 2019

With the help of learning analytics, MOOCs have wider potential to succeed in learning through promoting self-regulated learning (SRL). The current study aims to apply and validate visualization design guidelines for a MOOC dashboard to enhance such SRL capabilities based on learning analytics. To achieve the research objective, a MOOC dashboard prototype, LM-Dashboard, was designed and developed, reflecting the visualization design guidelines to promote SRL. Then, both expert and learner participants evaluated LM-Dashboard through iterations to validate the visualization design guidelines and perceived SRL effectiveness. The results of expert and learner evaluations indicated that most of the visualization design guidelines on LM-Dashboard were valid and some perceived SRL aspects such as monitoring a student's learning progress and assessing their achievements with time management were beneficial. However, some features on LM-Dashboard should be improved to enhance SRL aspects related to achieving their learning goals with persistence. The findings suggest that it is necessary to offer appropriate feedback or tips as well as to visualize learner behaviors and activities in an intuitive and efficient way for the successful cycle of SRL. Consequently, this study contributes to establishing a basis for the visual design of a MOOC dashboard for optimizing each learner's SRL.

• Journal of the Korea Society of Computer and Information
• /
• v.9 no.3
• /
• pp.115-120
• /
• 2004

As a part of working on development of the learning management system, a adaptive learning management system which is able to provide individual learner with different learning contents or paths customized to learner's learning behaviors by expanding SCORM was proposed in this dissertation. In terms of instructional technology interrelated with technology of CBI and ITS, new learning environments and learner preferences were analyzed. A related laboratory system was implemented by packaging a process on how to expand the meta data for contents and a process on how to utilize the web-based learning contents dynamically. In order to evaluate the usability of the implemented system, a sample content was provided to some selected learners and their learning achievement resulted from the new learning environment was analysed. A result of the experiment indicated that the adaptive learning management system proposed in this dissertation could provide every learner with the different content tailored to their individual learning preference and behavior. and it worked also to promote the learning performance of every learner.

• Journal of Korea Multimedia Society
• /
• v.5 no.3
• /
• pp.290-297
• /
• 2002

The appearance of web technology has accelerated the role of the application of multimedia technology, computer communication technology and multimedia application contents. Recently WBI model which is based on web has been proposed in the part of the new activity model of teaching-teaming. How to learn and evaluate is required to consider individual learner's learning level. And it is recognized that the needs of the efficient and automated education agents in the web-based instruction is increased But many education systems that had been studied recently did not service fluently the courses which learners had been wanting and could not provide the way for the learners to study the learning weakness which is observed in the continuous feedback of the course. In this paper we propose design of multi-module system for course scheduling of learner-oriented using weakness analysis algorithm. First proposed system monitors learner's behaviors constantly, evaluates them, and calculates his accomplishment and weakness. From this weakness the multi-agent prepares the learner a suitable course environment to strengthen his weakness. Then the learner achieves an active and complete teaming from the repeated and suitable course.