Unpacking the intertemporal impact of self-regulation in a blended mathematics environment

With the arrival of fine-grained log-data and the emergence of learning analytics, there may be new avenues to explore how Self-Regulated Learning (SRL) can provide a lens to how students learn in blended and online environments. In particular, recent research has found that the notion of time may be an essential but complex concept through which students make (un)conscious and self-regulated decisions as to when, what, and how to study. This study explored distinct clusters of behavioural engagement in an online e-tutorial called Sowiso at different time points (before tutorials, before quizzes, before exams), and their associations with self-regulated learning strategies, epistemic learning emotions, activity learning emotions, and academic performance. Using a cluster analysis on trace data of 1035 students practicing 429 online exercises in Sowiso, we identified four distinct cluster of students (e.g. early mastery, strategic, exam-driven, and inactive). Further analyses revealed significant differences between these four clusters in their academic performance, step-wise cognitive processing strategies, external self-regulation strategies, epistemic learning emotions and activity learning emotions. Our findings took a step forward towards personalised and actionable feedback in learning analytics by recognizing the complexity of how and when students engage in learning activities over time, and supporting educators to design early and theoretically informed interventions based on learning dispositions

What do distance learning students seek from student analytics?

This study explores the perspectives of distance learners about student-facing learning analytics. Nineteen middle-aged, white online students answered eight forum questions about a hypothetical scenario of a student who struggles to balance work and study and who was given access to a learning analytics dashboard. The dashboard presented comparative performance and engagement information and personalised study recommendations. Findings showed that study recommendations were highly favoured by students whereas peer comparisons were mostly viewed as not useful and demotivating

USING COMMUNICATION AND COLLABORATION TOOLS IN VIRTUAL LEARNING ENVIRONMENTS MOODLE FOR MATHEMATICS IN PRIMARY SCHOOL

With the advancement of information and communication technologies, teaching mathematics in a real-life classroom is combined with teaching in a virtual learning environment (VLE). It is important to determine how a primary school teacher can use VLE communication and collaboration tools to teach mathematics primary school students. Participants – 4th grade students ((n = 51). Access to quantitative studies has been chosen for the study. Methods of study: Analysis of scientific literature, testing, descriptive statistics, and inference statistics. Data from the pilot study and the educational experiment were processed using version 23 of the IBM SPSS Statistical Package for Social Sciences. The normality of the variable distribution was tested using the Shapiro-Wilk test. Throughout the research, decisions are taken at a value a = 0.05. Study adhered to the fundamental principles of the European Code of Conduct for Study Ethics. The curator of the education was in contact with the students and their parents by e-mail and using the VLE communication and collaboration tools (messages, forums, feedback). The aim was to find out whether the number of emails and messages sent by the curator affected the students’ learning time in the VLE. The hypothesis of zero Pearson coefficient equality in the population is checked. There was a statistically significant weak relationship between the number of emails sent by the curator of the curriculum, the number of messages for students and the time spent by the student for the lessons of the curriculum. There was a mean relationship in the boy’s group, but there was no statistically significant relationship in girls’ group. There was also a statistically significant weak relationship between e-mails sent by the curriculum curator, the number of messages sent to students and the evaluation of the lessons of the curriculum. There was an average relationship in the boy’s group, but in the girl’s group there was no statistically significant relationship between the emails sent by the tutor, the number of messages to students and the evaluation of the lessons of the curriculum. This confirms the theory of constructivism that VLE is suitable for education because teachers can act as learning facilitators to communicate with each other during learning

BLENDED LEARNING IN TEACHING MATHEMATICS

The background of this research was the development of blended learning in teaching mathematics. This study aimed to determine the benefits of blended learning in teaching mathematics by analyzing previous research. The method in this study is a systematic literature review (SLR), it descriptive based survey in the form of an analysis of 25 articles from the Science Direct database in the 2010-2020 period. The results showed that there are many benefits of blended learning in mathematic, which includes:  to improve mathematical thinking skills, develop good perceptions, improve learning outcomes, increase self-regulation, increase thinking/problem-solving skills, improve communication skills, increase student participation, simplify the assessment process, increase computational thinking skills, and critical thinking skills. The most significant benefit of blended learning is student learning outcomes, shown in 52% of the articles. The research implies the importance of supporting teachers in identifying the objectives of blended learning.

Effective usage of Learning Analytics: What do practitioners want and where should distance learning institutions be going?

The implementation of learning analytics may empower distance learning institutions to provide real-time feedback to students and teachers. Given the leading role of the Open University UK (OU) in research and application of learning analytics, this study aims to share the lessons learned from the experiences of 42 participants from a range of faculty, academic and professional positions, and expertise with learning analytics. Furthermore, we explored where distance learning institutions should be going next in terms of learning analytics adoption. The findings from the Learning Analytics User Stories (LAUS) workshop indicated that four key areas where more work is needed: communication, personalisation, integrated design, and development of an evidence-base. The workshop outputs signalled the aspiration for an integrated analytics system transcending the entire student experience, from initial student inquiry right through to qualification completion and into life-long learning. We hope that our study will spark discussion on whether (or not) distance learning institutions should pursue the dream of an integrated, personalised, and evidence-based learning analytics system that clearly communicates useful feedback to staff and students, or whether this will become an Orwellian nightmare

Literature Review: Mathematical Creative Thinking Ability, and Students’ Self Regulated Learning to Use an Open Ended Approach

The ability to think creatively and self regulated learning is very important in learning mathematics, in order to train students to develop their creativity. But in reality, mathematics learning that is currently happening has not been able to develop mathematical creative thinking skills and increase students’ self-regulated learning. The purpose of this study is to examine the relationship between the open-ended approach and the ability to think creative mathematically and self-regulated learning and how to implement it in school in the perspective of literature review. The type of research used is literature review, where articles are collected using search engines such as EBSCO, Science direct, and Scopus. Based on the articles collected, the results show that: 1. Mathematics learning to use an open-ended approach has an effect on increasing students ‘creative thinking skills and self-regulated learning, 2. The open-ended approach is higher in increasing students’ creative thinking abilities compared to using a conventional approach, 3. The open-ended approach to learning has the following steps: a) the teacher organizes students in learning activities; b) the teacher exposes students to open problems; c) the teacher guides and directs students in solving problems with various solutions and various answers; d) students present their work and compare with the work of other students in front of the class; and e) students conclude the subject matter, which is guided by the teacher. 4. To apply open-ended learning, it is necessary to prepare learning that requires high creativity for a teacher

Once more on the rollercoaster: Loses and gains from the rapid shift to online delivery during Covid

A global rapid shift to online delivery in higher education due to the Covid19 pandemic resulted in students and lecturers pivoting into a new learning environment, in many cases overnight. Our research nested within an Irish university explores how such a rapid educational delivery shift affected both students and lecturers offering a unique dual perspective and input into the changing roles of students and lecturers due to Covid19. Our research design focused on open-ended surveys of 83 M.Sc. postgraduate students and their five lecturers in five modules, followed by qualitative data collected through 34 in-depth interviews. The findings illustrate a complex narrative of self-regulation and challenge for both students and lecturers both needing to adjust to a new educational experience. The main findings is that there is a core challenge in the repositioning of the student and lecturer roles in a new educational ecosystem which needs to be both understood and managed to gain maximum benefit from this rapid and unprecedented change

Individual differences in the preference for worked examples: lessons from an application of dispositional learning analytics

Worked-examples have been established as an effective instructional format in problem-solving practices. However, less is known about variations in the use of worked examples across individuals at different stages in their learning process in student-centred learning contexts. This study investigates different profiles of students’ learning behaviours based on clustering learning dispositions, prior knowledge, and the choice of feedback strategies in a naturalistic setting. The study was conducted on 1,072 students over an eight-week long introductory mathematics course in a blended instructional format. While practising exercises in a digital learning environment, students can opt for tutored problem-solving, untutored problem-solving, or call worked examples. The results indicated six distinct profiles of learners regarding their feedback preferences in different learning phases. Finally, we investigated antecedents and consequences of these profiles and investigated the adequacy of used feedback strategies concerning ‘help-abuse’. This research indicates that the use of instructional scaffolds as worked-examples or hints and the efficiency of that use differs from student to student, making the attempt to find patterns at an overall level a hazardous endeavour

Understanding self-regulation strategies in problem-based learning through dispositional learning analytics

In the ongoing discussion about how learning analytics can effectively support self-regulated student learning and which types of data are most suitable for this purpose, this empirical study aligns with the framework who advocated the inclusion of both behavioral trace data and survey data in learning analytics studies. By incorporating learning dispositions in our learning analytics modeling, this research aims to investigate and understand how students engage with learning tasks, tools, and materials in their academic endeavors. This is achieved by analyzing trace data, which captures digital footprints of students’ interactions with digital tools, along with survey responses from the Study of Learning Questionnaire (SLQ), to comprehensively examine their preferred learning strategies. Additionally, the study explores the relationship between these strategies and students’ learning dispositions measured at the start of the course. An innovative aspect of this investigation lies in its emphasis on understanding how learning dispositions act as antecedents and potentially predict the utilization of specific learning strategies. The data is scrutinized to identify patterns and clusters of such patterns between students’ learning disposition and their preferred strategies. Data is gathered from two cohorts of students, comprising 2,400 first year students. This analytical approach aims to uncover predictive insights, offering potential indicators to predict and understand students’ learning strategy preferences, which holds value for teachers, educational scientists, and educational designers. Understanding students’ regulation of their own learning process holds promise to recognize students with less beneficial learning strategies and target interventions aimed to improve these. A crucial takeaway from our research underscores the significance of flexibility, which entails the ability to adjust preferred learning strategies according to the learning environment. While it is imperative to instruct our students in deep learning strategies and encourage autonomous regulation of learning, this should not come at the expense of acknowledging situations where surface strategies and controlled regulation may prove to be more effective