MOOC VİDEO ETKİLEŞİMİNDEKİ DAVRANIŞ ÖRÜNTÜLERİNİN KÜMELEME YAKLAŞIMI İLE BELİRLENMESİ

Year 2024, Volume: 14 Issue: 2, 198 - 217, 26.07.2024

Erkan Er , Gökhan Akçapınar , Gamze Sökücü

https://doi.org/10.17943/etku.1367188

Abstract

Videolar, ders içeriğini iletmek ve temel kavramları etkili bir şekilde öğretmek için kitlesel açık çevrimiçi derslerin temel bileşenleridir. Literatür, video izleme ile bu kitlesel derslerdeki öğrencilerin başarısı arasındaki bağlantı konusunda güçlü ve tutarlı bulgular sunsa da video izleme davranışı üzerine yapılan araştırmalar hala sınırlı ve yeni gelişmekte olan bir alandır. Bu araştırma makalesi, bir kitlesel açık çevrimiçi dersindeki video izleme aktivitelerindeki davranışsal desenleri tanımlayarak bu desenlerin başarı ve başarısızlıkla ilişkisini ortaya çıkarmayı amaçlamaktadır. Özellikle farklı bağlamlarda kullanılabilmesi ve uygulanabilmesi amacıyla temel ve yaygınlaştırılabilir video izleme metrikleri kullanılmıştır. Öğrencilerin bir öğrenme oturumu süresince farklı video görüntüleme davranışları gösterebileceği kabul edilerek, önceki araştırmalardan farklı olarak kümeleme analizi öğrenci özelinde değil oturum düzeyinde gerçekleştirilmiştir. Kümeleme analizi sonucunda üç davranışsal desen kümesi ortaya çıkmıştır: statik görüntüleme (en yaygın davranış), öğrencilerin minimum etkileşimle videoları izlediği durum; katılımlı görüntüleme, oynatma ve duraklama olaylarının sık olduğu durum; ve odaklı görüntüleme (en az rastlanan desen), özellikle belirli bir bilgiyi arama durumu. Statik görüntülemenin hâkim olduğu video oturumları hem başarılı hem de başarısız öğrenciler arasında yaygın olarak gözlemlenmiştir. Ancak katılımlı görüntüleme oturumları veya odaklı görüntüleme oturumları ise en çok başarılı öğrenciler tarafından sergilenmiştir. Ayrıca başarılı öğrencilerin birden fazla görüntüleme davranışı sergilediği saptanmıştır. Bu bulgu, öğrencilerin videoları izlerken çeşitli sayıda strateji uygulama çabalarını göstermektedir. Bulgulara dayalı olarak, video tabanlı öğrenmeyi destekleyen diğer çevrimiçi öğrenme platformlarının tasarımı için pratik öneriler paylaşılmıştır.

Keywords

kümeleme, video tabanlı öğrenme, video etkileşimleri, öğrenme analitiği, Kitlesel açık çevrimiçi ders

IDENTIFYING BEHAVIORAL PATTERNS IN MOOC VIDEO ENGAGEMENT USING CLUSTERING APPROACH

Abstract

Videos are the core components of MOOCs for delivering course content and teaching the core concepts effectively. While the literature provided strong and consistent evidence regarding the link between video engagement and the success in MOOCs, the research on video engagement behavior is still emerging and in demand of further research. This research aims to contribute to the literature by identifying behavioral patterns of video engagement in a MOOC and reveal the association of these patterns with success and failure. In particular, we employed simple video engagement metrics with an attempt to identify clusters of behavioral patterns that can be applied to different contexts. Acknowledging that students may exhibit varied engagement behaviors across study sessions, a session-level clustering analysis was performed, differently from previous research. After applying K-Means clustering algorithm, three clusters of behavioral patterns were identified: static viewing (the most predominant behavior), in which students viewed videos with minimal interactions; engaged viewing, involving high frequency of play and pause events; and focused viewing (the least frequent pattern), which involved mainly seeking the video for specific information. While video sessions with static viewing were very common among both high and low achieving students, most engaged-viewing sessions or focused-viewing sessions consistently belonged to the successful students. In addition, successful students were found to demonstrate multiple viewing behaviors, suggesting their effort in using multiple strategies while watching videos. Based on the findings, the paper discusses implications for the design of MOOCs and other online learning platforms that support video-based learning.

Keywords

massive open online course, video analytics, clustering, video-based learning

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