scholarly journals Combining Checkpoint and Process Learning Analytics to Support Learning Design Decisions in Blended Learning Environments

2020 ◽  
Vol 7 (3) ◽  
pp. 33-47
Author(s):  
Rogers Kaliisa ◽  
Anders Kluge ◽  
Anders I. Mørch
Keyword(s):  
Blended Learning ◽  
Learning Environments ◽  
Learning Analytics ◽  
Learning Design ◽  
Learning Behaviour ◽  
Teacher Experiences ◽  
Design Decisions ◽  
Process Analytics ◽  
Process Learning ◽  
Multiple Levels

Learning analytics (LA) constitutes a key opportunity to support learning design (LD) in blended learning environments. However, details as to how LA supports LD in practice and information on teacher experiences with LA are limited. This study explores the potential of LA to inform LD based on a one-semester undergraduate blended learning course at a Norwegian university. Our findings indicate that creating valuable connections between LA and LD requires a detailed analysis of student checkpoints (e.g., online logins) and process analytics (e.g., online content and interaction dynamics) to find meaningful learning behaviour patterns that can be forwarded to teachers in retrospect to support the redesign of courses. Moreover, the teachers in our study found the LA visualizations to be valuable for understanding student online learning processes, but they also requested the timely sharing of aggregated LA visualizations in a simple, easy-to-interpret format, yet detailed enough to be informative and actionable. We conclude the paper by arguing that the potential of LA to support LD is improved when multiple levels of LA are considered.

scholarly journals Modelling Teacher Practices to Apply Learning Analytics as a Metacognitive Tool in Learning to Enhance Student Success

2020 ◽  
pp. 113-123
Author(s):  
Rasa Greenspon ◽  
Margarita Teresevičienė ◽  
Justina Naujokaitienė
Keyword(s):  
Student Success ◽  
Learning Environments ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Teacher Practices ◽  
Educational Technologies ◽  
Learning Design ◽  
Process Learning ◽  
Contemporary Context

In the contemporary context, adoption of educational technologies has become inevitable. In virtual learning environments, teachers are not only exploring new ways of teaching, e.g. blended or online, but also incorporating various tools and strategies in order to facilitate the learning/teaching process. Learning analytics has received a lot of attention as it offers a support to teachers in monitoring students’ performance and making decisions regarding pedagogical approaches and techniques that would enhance learning and fulfil students’ realtime needs. In this research, a case study of university online or blended learning courses investigates the usage of learning analytics as a metacognitive tool to analyse how teaching and learning as well as learning design may be improved in order to enhance student success.

scholarly journals Informing learning design through analytics: Applying network graph analysis

2018 ◽  
Vol 34 (2) ◽  
Author(s):  
Dirk Ifenthaler ◽  
David Gibson ◽  
Eva Dobozy
Keyword(s):  
Learning Environments ◽  
Learning Analytics ◽  
Graph Analysis ◽  
Learning Design ◽  
Digital Learning ◽  
Time Data ◽  
Network Graph ◽  
Dynamic Learning ◽  
The Impact

Learning design has traditionally been thought of as an activity occurring prior to the presentation of a learning experience or a description of that activity. With the advent of near real-time data and new opportunities of representing the decisions and actions of learners in digital learning environments, learning designers can now apply dynamic learning analytics information on the fly in order to evaluate learner characteristics, examine learning designs, analyse the effectiveness of learning materials and tasks, adjust difficulty levels, and measure the impact of interventions and feedback. In a case study with 3550 users, the navigation sequence and network graph analysis demonstrate a potential application of learning analytics design. Implications based on the case study show that integration of analytics data into the design of learning environments is a promising approach.

scholarly journals Student Online Activity in Blended Learning: A Learning Analytics Perspective of Professional Teacher Education Studies in Finland

SAGE Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 215824402110566
Author(s):  
Arto O. Salonen ◽  
Annukka Tapani ◽  
Sami Suhonen
Keyword(s):  
Teacher Education ◽  
Distance Learning ◽  
Blended Learning ◽  
Learning Environments ◽  
Learning Analytics ◽  
Digital Learning ◽  
Online Activity ◽  
Professional Teacher ◽  
Digital Learning Environments ◽  
The Impact

Distance learning is rapidly gaining ground globally. In this case study, we focused on professional (vocational) teacher education (PTE) student online activity in a blended learning context. We applied learning analytics (LA) to identify students’ ( n = 19) online study patterns. Our key interest was in determining when and what kinds of online activity and behavior PTE students engage in during their studies. We applied quantitative content analysis to analyze the students’ behavior. Moodle’s event log enabled us to identify active hours and days, variation in use of learning materials, the impact of interventions, and stumbling blocks to student learning in the study unit. Based on our data, educator availability is an essential factor for good student engagement in digital learning environments. Interaction forums are important for PTE students effective learning. Monday and Tuesday afternoons are the most effective times for educators to be available for PTE students. There is a clear need for contact learning in professional teacher education, even when operating in digital learning environments. It plays an essential role in keeping students’ activity alive. It could be beneficial to plan a post-process for students who do not graduate as planned, including regular group meetings for supporting studies, receiving guidance, and meeting peers. PTE students’ behavior in a distance learning environment in the context of blended learning follows Zipf’s law, which models the occurrence of distinct objects in particular sorts of collections.

scholarly journals Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis

2021 ◽  
Vol 37 (2) ◽  
pp. 81-95
Author(s):  
Changqin Huang ◽  
Zhongmei Han ◽  
Ming Li ◽  
Xizhe Wang ◽  
Wenzhu Zhao
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Blended Learning ◽  
Learning Environments ◽  
Learning Process ◽  
Learning Analytics ◽  
The Other ◽  
Postgraduate Students ◽  
Learning Contexts ◽  
Social Emotion

Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning stages of blended learning. The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning contexts. Particularly in relation to deep interactions, student sentiments might change from negative to insightful ones. In contrast, the sentiment network built from social-emotion interactions shows stronger connections in joking-positive and joking-negative sentiments than the other two interaction levels. Most notably, the changes of co-occurrence sentiment reveal the three periods in a blended learning process, namely initial, collision and sublimation, and stable periods. The results in this study revealed that students’ sentiments evolved from positive to confused/negative to insightful.

Blast Furnace In Engineering Education

2014 ◽  
Vol 1 (1) ◽  
pp. 127-130 ◽  
Author(s):  
Valentin Petrescu ◽  
Florian Popescu ◽  
Alina Gligor
Keyword(s):  
Blast Furnace ◽  
Human Computer Interaction ◽  
Blended Learning ◽  
Engineering Education ◽  
Learning Environments ◽  
Learning Method ◽  
Quality Of Presentation ◽  
Computer Interaction

AbstractUsing blended learning method, Blast Furnace subject was analysed inside the DidaTec Project. The analysed factors were the quality of presentation, quantity of information per page and human – computer interaction. The analysis shows the preference of students to work with different learning environments.

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