LAView: Learning Analytics Dashboard Towards Evidence-based Education

The 9th International Learning Analytics and Knowledge (LAK) Conference : March 4-8, 2019, Tempe, Arizona, USALearning analytics dashboards (LAD) have supported prior finds that visualizing learning behavior helps students to reflect on their learning. We developed LAViEW, a LAD that can be easily integrated with different learning environments through LTI. In this paper, we focus on the context of eBook-based learning and present an overview of the indicators of engagement that LAView visualizes. Its integrated email widget enables the teacher to directly send personalized feedbacks to selected cohorts of students, clustered by their engagement scores. These interventions and dashboard interactions are further tracked to extract evidence of learning

MoodleMiner: Data Mining Analysis Tool for Moodle Learning Management System

The purpose of this study is to develop a tool through which non-experts can carry out basic data mining analyses on logs they obtained via Moodle Learning Management System. The study also includes the findings obtained by applying the developed tool on a data set from a real course. The developed tool automatically extracts the features regarding student interactions with the learning system by using their click-stream data, and analyzes this data by using the data mining libraries available in the R programming language. The tool has enabled the users who do not have any expertise in data mining or programming to automatically carry out data mining analyses. The information generated by the tool will help researchers and educators alike in grouping students by their interaction levels, determining at-risk students, monitoring students' interaction levels, and identifying important features that impact students’ academic performances. The data processed by the tool can also be exported to be used in various other analyses. In the future versions of the tool, it is planned to add different analyzes such as association rule mining, sequential pattern mining etc

Çevrimiçi Dersler için Video Analitik Aracının Tasarlanması ve Geliştirilmesi

Çevrimiçi öğrenme ortamları, öğrencilerin içeriklerle ve forum, viki vb. aktivitelerle ilgili her türlü etkileşimine ilişkin (bakma, silme, ekleme, güncelleme vb.) bilgileri veri tabanlarında kayıt etmektedir. Bu veriler, öğrenme süreçlerinin daha iyi anlaşılması ve eğitsel problemlerin çözümü konusunda öğrenme analitiği ve eğitsel veri madenciliği araştırmacılarının başvurduğu önemli veri kaynaklarıdır. Çevrimiçi öğrenme ortamlarında video tabanlı öğrenme materyallerinin kullanımının artması ile birlikte bu etkileşimlerin önemli bir bölümü videolar üzerinde gerçekleşmeye başlamıştır. Ancak, mevcut öğrenme yönetim sistemleri video izleme davranışlarının kayıt edilmesine ve analiz edilmesine olanak sağlamamaktadır ya da sınırlı analizler sunmaktadır. Yapılan çalışmalar ise bu verilerin analizi ile öğrencilerin video izleme davranışlarının anlaşılması ve video tabanlı ders materyallerinin geliştirilmesi konusunda önemli bilgilerin elde edilebileceğini göstermektedir. Araştırmacılar tarafından öğrencilerin video etkileşimlerini kaydetmeye olanak sağlayacak bir video oynatıcı geliştirilmiştir. Bu çalışma kapsamında, geliştirilen video oynatıcının teknik özelliklerine, araç sayesinde elde edilen etkileşim verilerine ve aracın uygulaması sonucu elde edilen verilerin analizine ilişkin bilgilere yer verilmiştir

Hipermetinsel Ortamlarda Önbilgi Düzeylerinin Gezinim Profilleri Üzerine Etkisi

The purpose of this study is to analyze how readers with varying prior knowledge navigate in hypertext learning environments. A standardized networked-structure hyper textual environment was developed by the first author and it was used as the learning material. Readers’ navigation paths and time duration on each page were recorded in log files. In addition, readers’ eye movements were also recorded during their navigation. Data analyzes showed that readers’ navigation patterns were differed across their prior knowledge levels. Low prior knowledge (LPK) readers felt more disoriented than high level prior knowledge (HPK) readers. Moreover, LPK readers were observed to navigate at the surface, rather than deep level. Navigational patterns and eye movement data also supported these findings.Bu çalışmada, Visual Basic programlama dili kullanılarak veritabanı işlemleri yapma ile ilgili farklı önbilgi düzeyine sahip bireylerin hipermetinsel ortamlarda gerçekleştirdikleri öğrenme amaçlı gezinim analiz edilmiştir. Eğitim materyali olarak araştırmacılar tarafından ağsal yapıda tasarlanan tek tip hipermetinsel ortam kullanılmıştır. Öğrencilerin gerçekleştirdikleri gezinim süresince izledikleri yol ve sayfalarda geçirdikleri süreler sunucu bilgisayar üzerinde log dosyalarına kaydedilmiştir. Aynı zamanda, bireylerin gezinme sürecindeki göz hareketleri de göz izleme cihazı yardımı ile kaydedilmiştir. Toplanan veriler analiz edildiğinde, önbilgi düzeyinin ağsal hipermetinsel ortamlardaki gezinim sürecine etkisi olduğu görülmüştür. Farklı önbilgiye sahip bireylerin gerçekleştirdikleri gezinimin yapısal olarak farklı olduğu; düşük önbilgiye sahip bireylerin kaybolmuşluk hissini daha fazla algıladığı ve düşükön bilgiye sahip bireylerin yüksek önbilgiye sahip bireylere göre daha yüzeysel gezinim gerçekleştirdikleri söylenebilir. Göz hareketleri ve gezinimlerinin yapısal analizi de bu bulguları desteklemektedir

Modeling Students’ Academic Performance Based on Their Interactions in an Online Learning Environment

The aim of this study is to model students' academic performance based on their interaction with the online learning environment designed by researchers. The dataset includes 10 input attributes extracted from students' learning activity logs. And as an output variable (class) final grades obtained by students in Computer Hardware course was used. The predictive performance of three different classification algorithms were tested (Naïve Bayes, Classification Tree, and CN2 rules) on dataset. Predictive performance of algorithms were compared in terms of Classification Accuracy (CA), and Area under the ROC Curve (AUC) metrics. All analysis were performed by using Orange data mining tool and models were evaluated by using ten-fold cross-validation. Results of analysis were presented as Confusion Matrix, Decision Tree, and IF-THEN rules. The experimental results indicate that the Naïve Bayes algorithm outperforms other classification algorithms in terms of CA and AUC metrics. On the other hand models which are generated by Classification Tree and CN2 algorithm are easy to understand for non-expert data mining users

Using a Summarized Lecture Material Recommendation System to Enhance Students’ Preclass Preparation in a Flipped Classroom

Research has revealed the positive effects of flipped classroom approaches on students’ learning engagement and performance compared with conventional lecture-based classrooms. However, because of a lack of out-of-class learning support, many students fail to comprehensively prepare the provided lecture materials before class. One promising solution to this problem is recommendation systems in the educational area, which have been instrumental in helping learners identify useful and relevant lecture materials that satisfy their learning needs. Thus, in this study, we propose a summarized lecture material recommendation system, which is integrated into an e-book reading system as an enhancement of the flipped classroom approach. This system helps students identify pages that contain essential knowledge that must be thoroughly studied before class. The proposed system was constructed on the basis of our previous work. In this study, a quasi-experiment was conducted in a graduate course that implemented the flipped classroom model: experimental group students learned with the proposed system, whereas the control group students had no access to the additional features. The findings of this study suggest that students who learn with the proposed recommendation system significantly outperform those who learn without the system in a flipped classroom in terms of their learning outcomes and engagement in preclass preparation

A Data Mining Approach To Students' Academic Performance Modeling In Online Learning Environment Based On Their Interaction Data

The purpose of this study is to model students' academic performance based on their interaction data in an online learning environment with the help of data mining techniques. As a part of the first research question, it was aimed to compare different classification algorithms in order to find the best algorithms and the best predictors of students' end of year academic performance. In another research question, it was investigated if it is possible or not to predict students' academic performance in previous weeks with the help of selected predictors and algorithm. The results of the study showed that 86% of students who failed and passed the course at the end of the year were classified correctly. When results related to early prediction of students' end of year academic performance are examined, it can be seen that by the third week, 74% of students can be accurately classified.Bu çalışma kapsamında çevrimiçi öğrenme ortamındaki etkileşim verileri kullanılarak öğrencilerin akademik performanslarının veri madenciliği yöntemleri ile modellenmesi amaçlanmıştır. Birinci araştırma sorusu kapsamında farklı sınıflama algoritmaları karşılaştırılarak öğrencilerin dönem sonu akademik performanslarını en iyi tahmin edecek algoritma ve değişkenlerin belirlenmesi amaçlanmıştır. Bir diğer araştırma problemi kapsamında bu değişkenler ve seçilen algoritma kullanılarak öğrencilerin akademik performanslarının daha önceki haftalarda tahmin edilip edilemeyeceği araştırılmıştır. Araştırma sonuçları öğrencilerin çevrimiçi öğrenme ortamındaki etkileşim verileri kullanılarak dönem sonundaki akademik performanslarının %86 oranında doğru olarak tahmin edilebileceğini göstermiştir. Bunun daha önceki haftalardan tahmin edilip edilemeyeceği ile ilgili analizler incelendiğinde ise üçüncü hafta gibi kısa bir sürede %74 oranında doğru olarak tahmin edilebileceği görülmüştür

Investigating Subpopulation of Students in Digital Textbook Reading Logs by Clustering

[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, USAThe increasing volume of student reading logs from virtual learning environment (VLE) provides opportunities for mining student’ engagement pattern in digital textbook reading. In order to mine and measure students’ engagement pattern, in this paper, we extract several students’ reading interaction variables from the digital textbook as metrics for the measurement of reading engagement. Moreover, in order to explore the presence of subpopulation of students that can be differentiated based on their engagement patterns and academic performances, we cluster students into different groups. Students are clustered based on their reading interactions such as total session of reading, total notes adding, etc. Accordingly, we identify students’ engagement patterns from different groups based on the clustering analysis results. Several student subpopulations such as low engagement high academic performances and low engagement low academic performances are identified based on students’ reading interaction characteristics by clustering analysis. The obtained results can be used to provide researchers with opportunities to intervene in the specific group of students and also an optimal choice for student grouping

Knowledge Map Creation for Modeling Learning Behaviors in Digital Learning Environments

[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, USAThere has been much research that demonstrates the effectiveness of using ontology to support the construction of knowledge during the learning process. However, the widespread adoption in classrooms of such methods are impeded by the amount of time and effort that is required to create and maintain an ontology by a domain expert. In this paper, we propose a system that supports the creation, management and use of knowledge maps at a learning analytics infrastructure level, integrating with existing systems to provide modeling of learning behaviors based on knowledge structures. Preliminary evaluation of the proposed text mining method to automatically create knowledge maps from digital learning materials is also reported. The process helps retain links between the nodes of the knowledge map and the original learning materials, which is fundamental to the proposed system. Links from concept nodes to other digital learning systems, such as LMS and testing systems also enable users to monitor and access lecture and test items that are relevant to concepts shown in the knowledge map portal

Learning Analytics to Share and Reuse Authentic Learning Experiences in a Seamless Learning Environment

Authentic learning experiences are considered to be a rich source for learning foreign vocabulary. Prevalent learning theories support the idea of learning from others’ authentic experiences. This study aims at developing a learning analytics solution to deliver the right authentic learning contents created by one learner to others in a seamless learning environment. Therefore, a conceptual framework is proposed to close the loops in the missing components of the current learning analytics framework. Data is captured and recorded centrally via a context-aware ubiquitous learning system which is a key component of a learning analytics framework. k-Nearest Neighbor (kNN) based profiling is used to measure the similarity of learners’ profiles. Authentic learning contents are shared and reused through re-logging function. This paper also discusses how two previously developed tools, namely learning log navigator and a three-layer architecture for mapping learners’ knowledge-level, are adapted to enhance the performance of the conceptual framework.[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, US