Topic outline
-
Welcome to the self-paced learning material about monitoring, supporting, and engaging students according to the evidence generated by digital technologies. This course is aimed at teachers in f-2-f and online contexts.
This material provides guidelines and recommendations on how teachers can design a course to generate data about learners’ behaviour and engagement with the learning content. Data-informed decision-making can improve teaching and learning and the redesign of new courses.
This learning material aims at empowering teachers in the use of learning analytics as evidence for data-informed decisions in learning design and course delivery facilitation. The multiple best practices and examples presented in the material provides different strategies and perspectives of implementation.
At the end of this self-training, you will familiarise yourself with what tools to use, what assessment strategies to adopt and what learning resources and/or activities to design to foster learners’ engagement.
Most of what you will learn is explained with reference to Moodle, but apply to other LMSs that provide similar features.
Make sure that you check the glossary to read the definitions of most relevant terms on the topic.
Also look at the Conceptual introduction document to find out more about the main concepts of the course. Optionally, you may watch a webinar (minute 4 to 36) on learning analytics and engagement presented at the 2022 European Online and Distance Learning Week (EODLW).
30 hours
Self-paced Free
-
Please use this map to learn about the course structure and decide if you would like to study the whole course or just separate topics.
-
Before starting the course, please, self-assess your knowledge about monitoring and analysing digital evidence.
-
-
In this section, we provide a conceptual introduction to the course, where all the key concepts are introduced and explained, as well as further readings are provided to learn more.
-
Introduction to the course’s main concepts
-
Please watch this 3-minute video which will provide you with a quick and visual overview of what LA is, what its key-elements are, its efficacy and the learning interventions that it allows to make.
-
-
Unit 1. Designing evidence-based teaching and learning strategies that foster self-regulated learning in VLE
In this unit we will show you how to:
- design metacognitive teaching and learning strategies (1.1.)
- how to set course activities settings to track learners’ engagement (1.2.)
- how to select and embed digital tools that capture data on learner progress (1.3.)
-
When designing evidence-based teaching and learning strategies, it is important not just to talk about what data can be collected, but to focus on how to design teaching and learning in a way that supports students' metacognitive learning. Knowing which metacognitive strategies support student engagement, performance and self-regulated learning can help you decide which tools to use to collect digital evidence on learners’ behaviour.
Read more...
-
Do you know how your students learn? Do students know how they learn? By helping students understand the learning strategies and methods that help them learn better, you can promote their engagement, performance and success. Metacognitive learning opportunities can help students take ownership of their learning. In addition, metacognitive knowledge fosters learners’ forethought and self-reflection, which are crucial for self-regulated learning.
Read more...
-
A research study on an online study course of English
-
As emphasised in the conceptual introduction, designing courses with digital technologies and tools, whether it is for assessment purposes or other teaching and learning purposes, generates a wide range of data. Therefore, it is very important for teachers to know how to configure a learning management system (LMS) and set up learning activities that would generate the type of digital evidence they require.
Read more... -
Reflective Learning, Teaching, and Assessment Based on Learning Analytics
-
The previous sections dealt with how to set up courses in order to be able to track learners’ engagement (1.1) and with how to design learning activities to generate evidence (1.2). In this subunit, we will focus on the list of tools that can be used to support teaching and learning and generate data. Moodle tools are examples of tools that might be integrated in a Learning Management System (LMS) (1.3.1). Then, we will show possible external tools that can be used to enhance the possibilities of active learning (1.3.2).
Read more... -
Designing multiple assessment strategies to collect and compare data in online course
-
Self-assess Your knowledge and understanding of the Topic 1.
-
- What learning activities would you design to encourage students to share their level of understanding of a topic/concept?
- Which learning activities can help you learn about students' prior experiences so that you can adapt learning content to them?
- Which activity settings should be configured to generate data on student engagement and learning design?
- Which activities can be designed to promote students' self-reflection on their learning process? What tools can support these activities?
- What activities would you plan to collect information on how to improve the course design?
-
This document is included for a peer-review phase only, so that experts could assess the quiz (the same questions are included in the quiz activity above). This document wil be deleted after the course will be peer-reviewed..
-
In this unit, we will show you how to:
- analyse and interpret available evidence on learner activity and progress to support engagement (2.1.)
- make data-informed teaching and learning interventions through reports (2.2.)
-
Figure 1. Learning Analytics as a Metacognitive Tool to Enhance Student Academic Success (Volungeviciene et al., 2021, p. 175)
According to the DigCompEdu Framework (Punie & Redecker, 2017), evidence analysis is a part of the assessment competence and means to generate, select, critically analyse and interpret digital evidence on learner activity, performance and progress in order to inform teaching and learning.
Read more... -
After designing activities to generate and collect data on learners’ engagement and progress (see Unit 1), you need to select the LA generated data that provide the information you need.
In this subunit we will provide you with a set of questions to answer depending on the objectives of the evidence analysis (2.1.1). Then, the next subsection (2.1.2) will deal with the reports that can inform you on how engaged learners are with the learning activities and how data can be used to support SRL.
The possibility to access and analyse LA generated data evidence should be seen as a solution to provide timely and personalised feedback to support students‘ self-regulated learning (SRL). When feedback is personalised, timely and targeted at developing learners’ SRL, it positively affects students‘ learning and time management strategies
-
DIANNA application project
-
In the learning design phase (unit 1), teachers need to design metacognitive teaching and learning strategies and decide which data will be necessary and important for the analysis. Then, they have to configure and set up the learning environment accordingly before the data are collected. In the teaching and learning phase (unit 2), teachers have to examine the generated data and analyse them. During this phase, they can spot possible learning design problems, identify students who are succeeding or struggling and observe tendencies in accessing learning resources. According to these data, they can make informed decisions and interventions.
Subunit 2.1. introduced a list of Moodle reports that generate data about learners’ engagement and learning progress. As outlined in 2.1, it is important to analyse data at the group/class level and individual student level and data can provide important insights on students’ engagement, learning progress and overall performance. This subunit focuses on generated data that could inform teachers about the teaching and learning interventions needed.
-
LA-based interventions to improve learners’ performance
-
Self-assess Your knowledge and understanding of the Topic 2.
-
- What reports can inform teachers and students about learning progress?
- What existing qualitative and quantitative data can be used to engage students?
- What data can be monitored and analysed to measure students' academic success?
- What pedagogical interventions would you plan in case students do not access and view compulsory learning resources?
- What would you do if the majority of students did not engage in some of the activities offered in the course?
-
This document is included for a peer-review phase only, so that experts could assess the quiz (the same questions are included in the quiz activity above). This document wil be deleted after the course will be peer-reviewed.
-
In this unit, we will explain and share insights on how to:
- foster student engagement (3.1.)
- increase students' engagement through customizable dashboards (3.2.)
-
Student engagement is promoted through active learning both in offline and online environments. This means that students are actively involved in assigned tasks, collaborate with their peers and deliver assignments on time. Learning design strategies to foster learners’ engagement include, but are not limited to, question-and-answer sessions, peer review and feedback, discussion, prompt questions, interactive lectures (in which students respond to or ask questions), quick writing assignments, hands-on activities, and experiential learning.
Read more... -
Betts, S.; Simons, S.; Liogier, V. (2020). Engaging learners in VLES. 25th of March, 2020, ETFOUNDATION.CO.UK/EDTECH. (YouTube) Online Webinar.
This is a 1-h webinar and its main aim is to support practitioners to use VLE tools to engage learners online. The webinar introduces the tools and the pedagogical approaches to motivate learners, it deals with Enhance Digital teaching Platform and supporting modules, it also deals with the badging process and the submissions. Some of these topics are not present in these learning materials. However, this optional webinar is a good way to increase your knowledge about online engagement and VLE.
-
As already mentioned before, it is important to design learning activities that generate evidence and foster learners’ metacognition and self-regulated learning (SRL) skills (see 1.1) and also to analyse and interpret the available evidence to support SRL and learners’ engagement (see 2.1 and 2.2).
In this section, we will describe how to promote student engagement and how to engage students in VLEs. Then, we will provide an overview of Moodle-based strategies that support course design and help engage students on the basis of LA, after having analysed learners’ evidence. Finally, we will explain how to engage students in reflecting and self-assessing their learning process.
-
-
-
This section deals with Learning Analytics Dashboard (LAD) a new promising tool to engage learners and favour SRL skills. While the first subsection provides a definition of LADs (3.2.1), the second section will provide you with a few guidelines to take the most out of this tool, taking into account that research on LADs is still in its infancy.
-
Learner Analytics Dashboards (LAD) as feedback and engagement tools for students
-
Self-assess Your knowledge and understanding of the Topic 3.
-
- What information would you use to understand how students experienced the course? Example: questions included in final feedback. What else?
- Teachers’ continuous input is critical for student engagement. How frequently do you think teachers should intervene and how?
- What would you do to adapt the activities to the students' prior knowledge? Think of some concrete examples!
- What instructional decision would you take if the analytical data reported a low level of engagement in students?
-
This document is included for a peer-review phase only, so that experts could assess the quiz (the same questions are included in the quiz activity above). This document wil be deleted after the course will be peer-reviewed.
-
This is a self-check tool to self-reflect on how You are prepared for student monitoring, support and engagement.
-
This is a self-check tool to self-reflect on how You are prepared for student monitoring, support and engagement.
You are encouraged to use the strategies (prompt statements) provided by the Training Materials :
-> to design the course to generate evidence-based data and support self-regulated learning
-> to monitor and analyse evidence-based data to support and engage learners and foster self-regulation.
-
-
For more information please contact institutional representatives of DigiProf project:
UOC (Spain) - Marcelo Fabián Maina, mmaina@uoc.edu
VMU (Lithuania) - Giedre Tamoliune, giedre.tamoliune@vdu.lt
DHBW (Germany) - UrSula Göz, ursula.goez@heilbronn.dhbw.de
UAVR (Portugal) - António Moreira, moreira@ua.pt
USilesia (Poland) - Marta Mamet-Michalkiewicz, marta.mamet-michalkiewicz@us.edu.pl -
