scholarly journals Curricular Concept Maps as Structured Learning Diaries: Collecting Data on Self-Regulated Learning and Conceptual Thinking for Learning Analytics Applications

2019 ◽  
Vol 6 (3) ◽  
Author(s):  
Ville Kivimäki ◽  
Joonas Pesonen ◽  
Jani Romanoff ◽  
Heikki Remes ◽  
Petri Ihantola
Keyword(s):  
Learning Analytics ◽  
Concept Maps ◽  
Time Series Data ◽  
Data Availability ◽  
Series Data ◽  
Structured Learning ◽  
Conceptual Thinking ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Selection Of

The collection and selection of the data used in learning analytics applications deserve more attention. Optimally, selection of data should be guided by pedagogical purposes instead of data availability. Using design science research methodology, we designed an artifact to collect time-series data on students’ self-regulated learning and conceptual thinking. Our artifact combines curriculum data, concept mapping, and structured learning diaries. We evaluated the artifact in a case study, verifying that it provides relevant data, requires a limited amount of effort from students, and works in different educational contexts. Combined with learning analytics applications and interventions, our artifact provides possibilities to add value for students, teachers, and academic leaders.

scholarly journals Training Self-Regulated Learning in the Classroom: Development and Evaluation of Learning Materials to Train Self-Regulated Learning during Regular Mathematics Lessons at Primary School

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Manuela Leidinger ◽  
Franziska Perels
Keyword(s):  
Primary School ◽  
Self Regulation ◽  
The Self ◽  
Structured Learning ◽  
Process Data ◽  
Learning Materials ◽  
Self Regulated Learning ◽  
Powerful Learning ◽  
Regulated Learning ◽  
Evaluation Of Learning

The aim of the intervention based on the self-regulation theory by Zimmerman (2000) was to promote a powerful learning environment for supporting self-regulated learning by using learning materials. In the study, primary school teachers were asked to implement specific learning materials into their regular mathematics lessons in grade four. These learning materials focused on particular (meta)cognitive and motivational components of self-regulated learning and were subdivided into six units, with which the students of the experimental group were asked to deal with on a weekly basis. The evaluation was based on a quasiexperimental pre-/postcontrol-group design combined with a time series design. Altogether, 135 fourth graders participated in the study. The intervention was evaluated by a self-regulated learning questionnaire, mathematics test, and process data gathered through structured learning diaries for a period of six weeks. The results revealed that students with the self-regulated learning training maintained their level of self-reported self-regulated learning activities from pre- to posttest, whereas a significant decline was observed for the control students. Regarding students’ mathematical achievement, a slightly greater improvement was found for the students with self-regulated learning training.

scholarly journals Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning

2021 ◽  
Author(s):  
Yizhou Fan ◽  
Wannisa Matcha ◽  
Nora’ayu Ahmad Uzir ◽  
Qiong Wang ◽  
Dragan Gašević
Keyword(s):  
Learning Analytics ◽  
High Performance ◽  
Process Mining ◽  
Learning Experience ◽  
Learning Design ◽  
Metacognitive Control ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
High Level ◽  
Evaluation Of Learning

AbstractThe importance of learning design in education is widely acknowledged in the literature. Should learners make effective use of opportunities provided in a learning design, especially in online environments, previous studies have shown that they need to have strong skills for self-regulated learning (SRL). The literature, which reports the use of learning analytics (LA), shows that SRL skills are best exhibited in choices of learning tactics that are reflective of metacognitive control and monitoring. However, in spite of high significance for evaluation of learning experience, the link between learning design and learning tactics has been under-explored. In order to fill this gap, this paper proposes a novel learning analytic method that combines three data analytic techniques, including a cluster analysis, a process mining technique, and an epistemic network analysis. The proposed method was applied to a dataset collected in a massive open online course (MOOC) on teaching in flipped classrooms which was offered on a Chinese MOOC platform to pre- and in-service teachers. The results showed that the application of the approach detected four learning tactics (Search oriented, Content and assessment oriented, Content oriented and Assessment oriented) which were used by MOOC learners. The analysis of tactics’ usage across learning sessions revealed that learners from different performance groups had different priorities. The study also showed that learning tactics shaped by instructional cues were embedded in different units of study in MOOC. The learners from a high-performance group showed a high level of regulation through strong alignment of the choices of learning tactics with tasks provided in the learning design. The paper also provides a discussion about implications of research and practice.

Using learning analytics to explore self-regulated learning in flipped blended learning music teacher education

2017 ◽  
Vol 50 (1) ◽  
pp. 114-127 ◽  
Author(s):  
Amanda P. Montgomery ◽  
Amin Mousavi ◽  
Michael Carbonaro ◽  
Denyse V. Hayward ◽  
William Dunn
Keyword(s):  
Teacher Education ◽  
Blended Learning ◽  
Learning Analytics ◽  
Music Teacher ◽  
Music Teacher Education ◽  
Self Regulated Learning ◽  
Regulated Learning

Learning Analytics and Technology Through Teaching

2021 ◽  
Author(s):  
Matthew Kaufman ◽  
Kristi Yuthas
Keyword(s):  
Learning Analytics ◽  
Data Analytics ◽  
New Technologies ◽  
Teaching Approach ◽  
Knowledge Gap ◽  
Technical Knowledge ◽  
Self Regulated Learning ◽  
Logical Sequence ◽  
Regulated Learning ◽  
Pedagogical Framework

Data analytics problems, methods and software are changing rapidly. Learning how to learn new technologies might be the most important skill for students to develop in an analytics course. We present a pedagogical framework that promotes self-regulated learning and metacognition and three student-driven assignments that can be used in accounting analytics and other courses that incorporate technology. The assignment can be used by faculty who do not have training in analytics. The assignments adopt a learn-through-teaching approach that helps students: 1) define a conceptual or technical knowledge gap; 2) identify resources available for filling that gap; 3) work independently to acquire the desired knowledge; 4) break knowledge into components and arrange in a logical sequence; and 5) reinforce knowledge by presenting to others in an accessible manner. These assignments equip students with confidence and capabilities that will enable them to keep up with advances in technology.

scholarly journals Relative effects of regional and global trade on carbon emissions in ECOWAS member countries

2019 ◽  
Vol 22 (1) ◽  
pp. 64-75
Author(s):  
Lukman O Oyelami
Keyword(s):  
Environmental Quality ◽  
Carbon Emissions ◽  
Time Series Data ◽  
Relative Effect ◽  
Data Availability ◽  
Series Data ◽  
Global Trade ◽  
Panel Ardl ◽  
Long Run ◽  
Short Run

The effect of trade on environmental quality has always been ambiguous in both developed and developing countries. This has prompted several country- and region-specific studies. It is against this background that this study seeks to investigate the effect of international trade on carbon emissions in the ECOWAS subregion in general and specifically determines the relative effect of regional and global trade on carbon emissions. To achieve this, time series data on trade and carbon emissions from 1970 to 2014 were employed for 14 ECOWAS member countries based on data availability and the data were duly subjected to required econometric tests to prevent spurious analysis. PMG/MG method of panel ARDL was adopted to estimate the relative effect of regional and global trade on carbon emissions and this is based on capability of the method to classify relationship into short-run and long-run and also solve endogenity issues. The results from model estimation show that effect of trade on environmental quality is a long-term phenomenon and basically support the view that trade has negative effect on environmental quality. However, regional trade is less harmful and it can guarantee improved environment quality in the long run. The study therefore recommend that countries in the region should trade more with one another especially in areas where they lack competiveness as this can better guarantee a more sustainable development for the entire subregion.

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