TALIS Presentation: The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules
The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules
http://www.sciencedirect.com/science/article/pii/S0747563216301327?np=y
Discussant SRHE Symposium "A cross-institutional perspective on merits and ch...Bart Rienties
In the UK, the introduction of the Teaching Excellence Framework (TEF) has increased interest in
appropriate and valid measurement approaches of learning gains in Higher Education. Learning gains
are defined as growth or change in knowledge, skills, and abilities of learners over time. While the UK
government and other organisations like HEFCE expect tremendous opportunities for learning gains
to “objectively” measure the value added of higher education across institutions, empirical evidence of
the robustness, reliability, and validity of learning gains literature outside the UK is mixed. At SRHE,
we will discuss the affordances, lived experiences, and limitations of using different measurements,
conceptualisations, and methodologies of learning gains. We aim to set an evidence-based agenda of
how HEIs can effectively start to measure and implement notions of learning gains, while at the same
time discussing potential limitations and caveats.
www.abclearninggains.com @learninggains
Are emotions driving better university courses?Bart Rienties
Wednesday 13 April, 18:30 - 19:30 (BST)
Do happier students make better learners? How much do our emotions dictate how we learn? And can we use this information to make university courses better?
These are just some of the questions we’ll be exploring as part of a special talk by some of the UK's leading academics in this area.
OU Technology Enhanced Learning experts are researching a concept called ‘analytics of emotions’ which means that in the future, devices like eye trackers and facial recognition software will analyse students’ emotional states when they are learning. These devices can gauge whether students are bored or frustrated by their online materials by the amount they sigh or frown.
The researchers predict in their annual Innovating Pedagogy report that that within the next 10 years, the design of university courses will be driven by how students interact socially and emotionally with their materials, peers, parents and teachers.
They will elaborate on what this means for higher education in their talk.
In the OpenMinds talk: Are emotions driving better university courses? the following topics will be investigated:
Dr Bart Rienties, Reader in Learning Analytics at the OU, will highlight the role of emotions in learning and question why they are often ignored.
Dr Ana Aznar, Postdoctoral Research Fellow, School of Psychology, University of Surrey, will explore how emotions develop in children and how they influence their learning.
Garron Hillaire, OU PhD student, Institute of Educational Technology will describe how his research categorises over 200 emotions related to learning. He will reveal how traces of student data are being used for emotional measurement. With these measures he will be leveraging the OU learning laboratories to validate the approach by examining physiological responses including facial muscle movement, heart rates, and galvanic skin sensors. Most importantly in the coming year these studies will expand to a University context to explore the role of emotion in Higher Education.
“The concept, “analytics of emotions” means that in the future, devices like eye trackers and facial recognition software will analyse students’ emotional states when they are learning,” said Dr Rienties. “These devices will track whether students find their content boring and frustrating, all of which can be fed back into course design. Emotions play a critical role in the learning and teaching process because they impact on learners’ motivation, self-regulation and academic achievement, so it is surprising that up to now, they have been mostly ignored in learning.”
SIRIKT Keynote: Learning Analytics: The good, the bad, or perhaps ugly?
The presentation will be the introduction of learning analytics, setting it in the context of big data and the increasing role of technology in learning, emphasising the role of analytics for supporting learning. Some examples will be given, and the points will be highlighted where we have the best evidence for learning analytics being helpful. The presentation will end with some suggestions – some practical, some conceptual – for how researchers and practitioners could move forward.
Dr. Bart Carlo Rienties is Reader in Learning Analytics at the Institute of Educational Technology at the Open University UK. He is programme director Learning Analytics within IET and Chair of Student Experience Project Intervention and Evaluation group, which focusses on evidence-based research on intervention of 15 modules to enhance student experience. As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He successfully led a range of institutional/national/European projects and received several awards for his educational innovation projects.
QAA Modelling and Managing Student Satisfaction: Use of student feedback to ...Bart Rienties
To what extent are institutions using insights from NSS and institutional surveys to transform their students’ experience?
What are the key enablers and barriers for integrating student satisfaction data with QA and QE
How are student experiences influencing quality enhancements
What influences students’ perceptions of overall satisfaction the most? Are student characteristics or module/presentation related factors more predictive than satisfaction with other aspects of their learning experience?
Is the student cohort homogenous when considering satisfaction key drivers? For example are there systematic differences depending on the level or programme of study?
Keynote address Analytics4Action Evaluation Framework: a review of evidence-...Bart Rienties
Bart Rienties is a Reader in Learning Analytics at the Institute of Educational Technology at the Open University UK. He is programme director Learning Analytics within IET and Chair of Analytics4Action project, which focuses on evidence-based research on interventions on OU modules to enhance student experience. As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He successfully led a range of institutional/national/European projects and received several awards for his educational innovation projects.
Discussant SRHE Symposium "A cross-institutional perspective on merits and ch...Bart Rienties
In the UK, the introduction of the Teaching Excellence Framework (TEF) has increased interest in
appropriate and valid measurement approaches of learning gains in Higher Education. Learning gains
are defined as growth or change in knowledge, skills, and abilities of learners over time. While the UK
government and other organisations like HEFCE expect tremendous opportunities for learning gains
to “objectively” measure the value added of higher education across institutions, empirical evidence of
the robustness, reliability, and validity of learning gains literature outside the UK is mixed. At SRHE,
we will discuss the affordances, lived experiences, and limitations of using different measurements,
conceptualisations, and methodologies of learning gains. We aim to set an evidence-based agenda of
how HEIs can effectively start to measure and implement notions of learning gains, while at the same
time discussing potential limitations and caveats.
www.abclearninggains.com @learninggains
Are emotions driving better university courses?Bart Rienties
Wednesday 13 April, 18:30 - 19:30 (BST)
Do happier students make better learners? How much do our emotions dictate how we learn? And can we use this information to make university courses better?
These are just some of the questions we’ll be exploring as part of a special talk by some of the UK's leading academics in this area.
OU Technology Enhanced Learning experts are researching a concept called ‘analytics of emotions’ which means that in the future, devices like eye trackers and facial recognition software will analyse students’ emotional states when they are learning. These devices can gauge whether students are bored or frustrated by their online materials by the amount they sigh or frown.
The researchers predict in their annual Innovating Pedagogy report that that within the next 10 years, the design of university courses will be driven by how students interact socially and emotionally with their materials, peers, parents and teachers.
They will elaborate on what this means for higher education in their talk.
In the OpenMinds talk: Are emotions driving better university courses? the following topics will be investigated:
Dr Bart Rienties, Reader in Learning Analytics at the OU, will highlight the role of emotions in learning and question why they are often ignored.
Dr Ana Aznar, Postdoctoral Research Fellow, School of Psychology, University of Surrey, will explore how emotions develop in children and how they influence their learning.
Garron Hillaire, OU PhD student, Institute of Educational Technology will describe how his research categorises over 200 emotions related to learning. He will reveal how traces of student data are being used for emotional measurement. With these measures he will be leveraging the OU learning laboratories to validate the approach by examining physiological responses including facial muscle movement, heart rates, and galvanic skin sensors. Most importantly in the coming year these studies will expand to a University context to explore the role of emotion in Higher Education.
“The concept, “analytics of emotions” means that in the future, devices like eye trackers and facial recognition software will analyse students’ emotional states when they are learning,” said Dr Rienties. “These devices will track whether students find their content boring and frustrating, all of which can be fed back into course design. Emotions play a critical role in the learning and teaching process because they impact on learners’ motivation, self-regulation and academic achievement, so it is surprising that up to now, they have been mostly ignored in learning.”
SIRIKT Keynote: Learning Analytics: The good, the bad, or perhaps ugly?
The presentation will be the introduction of learning analytics, setting it in the context of big data and the increasing role of technology in learning, emphasising the role of analytics for supporting learning. Some examples will be given, and the points will be highlighted where we have the best evidence for learning analytics being helpful. The presentation will end with some suggestions – some practical, some conceptual – for how researchers and practitioners could move forward.
Dr. Bart Carlo Rienties is Reader in Learning Analytics at the Institute of Educational Technology at the Open University UK. He is programme director Learning Analytics within IET and Chair of Student Experience Project Intervention and Evaluation group, which focusses on evidence-based research on intervention of 15 modules to enhance student experience. As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He successfully led a range of institutional/national/European projects and received several awards for his educational innovation projects.
QAA Modelling and Managing Student Satisfaction: Use of student feedback to ...Bart Rienties
To what extent are institutions using insights from NSS and institutional surveys to transform their students’ experience?
What are the key enablers and barriers for integrating student satisfaction data with QA and QE
How are student experiences influencing quality enhancements
What influences students’ perceptions of overall satisfaction the most? Are student characteristics or module/presentation related factors more predictive than satisfaction with other aspects of their learning experience?
Is the student cohort homogenous when considering satisfaction key drivers? For example are there systematic differences depending on the level or programme of study?
Keynote address Analytics4Action Evaluation Framework: a review of evidence-...Bart Rienties
Bart Rienties is a Reader in Learning Analytics at the Institute of Educational Technology at the Open University UK. He is programme director Learning Analytics within IET and Chair of Analytics4Action project, which focuses on evidence-based research on interventions on OU modules to enhance student experience. As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He successfully led a range of institutional/national/European projects and received several awards for his educational innovation projects.
SRHE2016: Multilevel Modelling of Learning Gains: The Impact of Module Partic...Bart Rienties
Jekaterina Rogaten1
, Bart Rienties1
, Denise Whitelock1
, Simon Cross1
, Allison Littlejohn1
, Rhona
Sharpe2
, Simon Lygo-Baker3
, Ian Scott2
, Steven Warburton3
, Ian Kinchin3
1The Open University UK, UK,
2Oxford Brooks University, UK,
3University of Surrey, UK
Research Domain: Learning, teaching and assessment (LTA)
In the UK, the introduction of the Teaching Excellence Framework (TEF) has increased interest in
appropriate and valid measurement approaches of learning gains in Higher Education. Usually
learning gains are measured using pre-post testing, but this study examines whether academic
performance can be effectively used as proxy to estimate students’ learning progress. Academic
performance of 21,192 online learners from two major faculties was retrieved from university
database. A three-level growth-curve model was estimated and results showed that 16% to 46% of
variance in students’ initial academic performance, and 51% to 77% of variance in their subsequent
learning gains was due to them studying at a particular module. In addition, the results illustrate that
students who studied in modules with initial high student achievements exhibited lower learning gains
than students learning in modules with low initial student achievements. The importance of
assessment and learning design for learning gains are outlined.
www.abclearninggains.com @learninggains
Keynote H818 The Power of (In)formal learning: a learning analytics approachBart Rienties
A special thanks to Avinash Boroowa, Simon Cross, Lee Farrington-Flint, Christothea Herodotou, Lynda Prescott, Kevin Mayles, Tom Olney, Lisette Toetenel, John Woodthorpe and others…A special thanks to Prof Belinda Tynan for her continuous support on analytics at the OU UK
Keynote EARLI SIG17 The power of learning analytics: a need to move towards n...Bart Rienties
Across the globe many institutions and organisations have high hopes that learning analytics can play a major role in helping their organisations remain fit-for-purpose, flexible, and innovative. According to Tempelaar, Rienties, and Giesbers (2015, p. 158) “a broad goal of learning analytics is to apply the outcomes of analysing data gathered by monitoring and measuring the learning process”. Learning analytics applications in education are expected to provide institutions with opportunities to support learner progression, but more importantly in the near future provide personalised, rich learning on a large scale (Rienties, Cross, & Zdrahal, 2016; Tempelaar et al., 2015; Tobarra, Robles-Gómez, Ros, Hernández, & Caminero, 2014).
Increased availability of large datasets (Arbaugh, 2014), powerful analytics engines (Tobarra et al., 2014), and skilfully designed visualisations of analytics results (González-Torres, García-Peñalvo, & Therón, 2013) mean that institutions may now be able to use the experience of the past to create supportive, insightful models of primary (and even real-time) learning processes (Arnold & Pistilli, 2012; Ferguson & Buckingham Shum, 2012; Papamitsiou & Economides, 2014). Substantial progress in learning analytics research relating to identifying at-risk students has been made in the last few years using a range of advanced computational techniques (e.g., Bayesian modelling, cluster analysis, natural language processing, machine learning, predictive modelling, social network analysis).
In this EARLI SIG17 keynote, I will argue that one of the largest challenges for learning analytics and wider educational research still lies ahead of us, and that one substantial and immediate challenge is how to put the power of learning analytics into the hands of researchers, teachers and administrators. While an increasing body of literature has become available regarding how institutions have experimented with small-scale interventions (Papamitsiou & Economides, 2014), to the best of our knowledge no comprehensive conceptual model, nested within a strong evidence-base, is available that describes how researchers, teachers and administrators can use learning analytics to make successful interventions in their own practice. In this keynote, I will use the development of a foundation of an Analytics4Action Evaluation Framework (A4AEF) that is being currently tested and validated at the largest university in Europe (in terms of enrolled learners), namely the UK Open University (OU, Calvert, 2014), as an example of the complexity of different, interlinked methodological and conceptual approaches.
The power of learning analytics for UCL: lessons learned from the Open Univer...Bart Rienties
Across the globe many institutions and organisations have high hopes that learning analytics can play a major role in helping their organisations remain fit-for-purpose, flexible, and innovative. Learning analytics applications in education are expected to provide institutions with opportunities to support learner progression, but more importantly in the near future provide personalised, rich learning on a large scale. In this seminar, we will discuss lessons learned from various learning analytics applications at the OU.
Global experiences with e-learning and dataBart Rienties
Pedagogically informed designs of learning are increasingly of interest to researchers in blended and online learning, as learning design is shown to have an impact on student behaviour and outcomes. Although learning design is widely studied, often these studies are individual courses or programmes and few empirical studies have connected learning designs of a substantial number of courses with learning behaviour. In this study we linked 151 modules and 111.256 students with students' behaviour (<400 million minutes of online behaviour), satisfaction and performance at the Open University UK using multiple regression models. Our findings strongly indicate the importance of learning design in predicting and understanding Virtual Learning Environment behaviour and performance of students in blended and online environments. In line with proponents of social learning theories, our primary predictor for academic retention was the time learners spent on communication activities, controlling for various institutional and disciplinary factors. Where possible, appropriate and well designed communication tasks that align with the learning objectives of the course may be a way forward to enhance academic retention.
The power of learning analytics to measure learning gains: an OU, Surrey and ...Bart Rienties
Learning gains has increasingly become apparent within the HE literature, gained traction in government policies in the UK, and are at the heart of Teaching Excellence Framework (TFL). As such, this raises a question to what extent teaching and learning environment can actually predict students’ learning gains using principles of learning analytics. In this presentation, which is joined work with University of Surrey and Oxford Brookes, I will focus on some preliminary findings based upon developing and testing an Affective-Behaviour-Cognition learning gains model using longitudinal approach. The main aim of the research is to examine whether learning gains occur on all three levels of Affective-Behaviour-Cognition model and whether any particular student or course characteristics can predict learning gains or lack of learning and dropout. For more info, see https://abclearninggains.com/
Learning design meets learning analytics: Dr Bart Rienties, Open UniversityBart Rienties
8th UK Learning Analytics Network Meeting, The Open University, 2nd November 2016
1) The power of 151 Learning Designs on 113K+ students at the OU?
2) How can we use learning design to empower teachers?
3) How can Early Alert Systems improve Student Engagement and Academic Success? (Amara Atif, Macquarie University)
4) What evidence is there that learning design makes a difference over time and how students engage?
Drivers of higher education institutions’ visibility: a study of UK HEIs soci...Kim Holmberg
Social media is increasingly used in higher education settings by researchers, students and institutions. Whether it is researchers conversing with other researchers, or universities seeking to communicate to a wider audience, social media platforms serve as a tools for users to communicate and increase visibility. Scholarly communication in social media and investigations about social media metrics is of increasing interest for scientometric researchers, and to the emergence of altmetrics. Less understood is the role of organizational characteristics in garnering social media visibility, through for instance liking and following mechanisms. In this study we aim to contribute to the understanding of the effect of specific social media use by investigating higher education institutions’ presence on Twitter. We investigate the possible connections between followers on Twitter and the use of Twitter and the organizational characteristics of the HEIs. We find that HEIs’ social media visibility on Twitter are only partly explained by social media use and that organizational characteristics also play a role in garnering these followers. Although, there is an advantage in garnering followers for those first adopters of Twitter. These findings emphasize the importance of considering a range of factors to understand impact online for organizations and HEIs in particular.
Track 02 - Educational innovation
Authors: José Carlos Sánchez Prieto, Susana Olmos Migueláñez and Francisco José García-Peñalvo
https://www.youtube.com/watch?v=rP98kYJZyp0&list=PLboNOuyyzZ879QIq5OTq3y3qE62GN4Api&index=5
ASCILITE Webinar: A review of five years of implementation and research in al...Bart Rienties
Date and time: Wednesday 20 September 2017 at 5pm AEST
Abstract: The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, we have been unpacking the complexity of instructional practices, as well as providing empirical evidence of how learning designs influence student behaviour, satisfaction, and performance. This seminar will discuss the implementation of learning design at the OU in the last 5 years, and reviews empirical evidence from several studies that have linked learning design with learning analytics. Recommendations are put forward to support future adoptions of the learning design approach, and potential research trajectories.
https://ascilite.org/get-involved/sigs/learning-analytics-sig/
www.bartrienties.nl
Learning outcome,Task and Topic analysis,Sequencing and chunking.Make a session plan( introduction, body, conclusion).
Linking of Learning Outcomes with Teaching, Learning Activities and Assessment.
Effect of Makerspace Professional Development Activities on Elementary & Midd...STEAM Learning Lab
Dissertation on the Effect of Makerspace Professional Development Activities on Elementary & Middle School Educator Perceptions of Integrating Technologies with STEM (science, technology, engineering, mathematics)
Personalization is an alternative to improve the learning process for an e-Learning environment. It is a useful strategy to adjust the student' needs based on their characteristics to make learning more effectively. In this study, we propose the step-function approach for personalization in e-learning. It provides the students with adopting the knowledge-ability factor (Novice, Average, or Good category) that matches with their learning materials levels (Level1, Level2, or Level3). The approach implemented into an e-learning which called SCELE-PDE and used as the experimental group in two stages with different scenarios. In the first, without a step-function approach, but the SCELE-PDE can identify an initial of student's ability to knowledge category. The second stage has used the approach to providing students with personalization in e-Learning to adapt learning material based on a knowledge category. As a result, the step-function approach has successfully to improve the student performance in the learning process during the course. Thus, the approach has shown an increase in the level of students’ knowledge. So, it can be used as a guide when designing an e-learning personalization for students to enhance learning and achievement.
SRHE2016: Multilevel Modelling of Learning Gains: The Impact of Module Partic...Bart Rienties
Jekaterina Rogaten1
, Bart Rienties1
, Denise Whitelock1
, Simon Cross1
, Allison Littlejohn1
, Rhona
Sharpe2
, Simon Lygo-Baker3
, Ian Scott2
, Steven Warburton3
, Ian Kinchin3
1The Open University UK, UK,
2Oxford Brooks University, UK,
3University of Surrey, UK
Research Domain: Learning, teaching and assessment (LTA)
In the UK, the introduction of the Teaching Excellence Framework (TEF) has increased interest in
appropriate and valid measurement approaches of learning gains in Higher Education. Usually
learning gains are measured using pre-post testing, but this study examines whether academic
performance can be effectively used as proxy to estimate students’ learning progress. Academic
performance of 21,192 online learners from two major faculties was retrieved from university
database. A three-level growth-curve model was estimated and results showed that 16% to 46% of
variance in students’ initial academic performance, and 51% to 77% of variance in their subsequent
learning gains was due to them studying at a particular module. In addition, the results illustrate that
students who studied in modules with initial high student achievements exhibited lower learning gains
than students learning in modules with low initial student achievements. The importance of
assessment and learning design for learning gains are outlined.
www.abclearninggains.com @learninggains
Keynote H818 The Power of (In)formal learning: a learning analytics approachBart Rienties
A special thanks to Avinash Boroowa, Simon Cross, Lee Farrington-Flint, Christothea Herodotou, Lynda Prescott, Kevin Mayles, Tom Olney, Lisette Toetenel, John Woodthorpe and others…A special thanks to Prof Belinda Tynan for her continuous support on analytics at the OU UK
Keynote EARLI SIG17 The power of learning analytics: a need to move towards n...Bart Rienties
Across the globe many institutions and organisations have high hopes that learning analytics can play a major role in helping their organisations remain fit-for-purpose, flexible, and innovative. According to Tempelaar, Rienties, and Giesbers (2015, p. 158) “a broad goal of learning analytics is to apply the outcomes of analysing data gathered by monitoring and measuring the learning process”. Learning analytics applications in education are expected to provide institutions with opportunities to support learner progression, but more importantly in the near future provide personalised, rich learning on a large scale (Rienties, Cross, & Zdrahal, 2016; Tempelaar et al., 2015; Tobarra, Robles-Gómez, Ros, Hernández, & Caminero, 2014).
Increased availability of large datasets (Arbaugh, 2014), powerful analytics engines (Tobarra et al., 2014), and skilfully designed visualisations of analytics results (González-Torres, García-Peñalvo, & Therón, 2013) mean that institutions may now be able to use the experience of the past to create supportive, insightful models of primary (and even real-time) learning processes (Arnold & Pistilli, 2012; Ferguson & Buckingham Shum, 2012; Papamitsiou & Economides, 2014). Substantial progress in learning analytics research relating to identifying at-risk students has been made in the last few years using a range of advanced computational techniques (e.g., Bayesian modelling, cluster analysis, natural language processing, machine learning, predictive modelling, social network analysis).
In this EARLI SIG17 keynote, I will argue that one of the largest challenges for learning analytics and wider educational research still lies ahead of us, and that one substantial and immediate challenge is how to put the power of learning analytics into the hands of researchers, teachers and administrators. While an increasing body of literature has become available regarding how institutions have experimented with small-scale interventions (Papamitsiou & Economides, 2014), to the best of our knowledge no comprehensive conceptual model, nested within a strong evidence-base, is available that describes how researchers, teachers and administrators can use learning analytics to make successful interventions in their own practice. In this keynote, I will use the development of a foundation of an Analytics4Action Evaluation Framework (A4AEF) that is being currently tested and validated at the largest university in Europe (in terms of enrolled learners), namely the UK Open University (OU, Calvert, 2014), as an example of the complexity of different, interlinked methodological and conceptual approaches.
The power of learning analytics for UCL: lessons learned from the Open Univer...Bart Rienties
Across the globe many institutions and organisations have high hopes that learning analytics can play a major role in helping their organisations remain fit-for-purpose, flexible, and innovative. Learning analytics applications in education are expected to provide institutions with opportunities to support learner progression, but more importantly in the near future provide personalised, rich learning on a large scale. In this seminar, we will discuss lessons learned from various learning analytics applications at the OU.
Global experiences with e-learning and dataBart Rienties
Pedagogically informed designs of learning are increasingly of interest to researchers in blended and online learning, as learning design is shown to have an impact on student behaviour and outcomes. Although learning design is widely studied, often these studies are individual courses or programmes and few empirical studies have connected learning designs of a substantial number of courses with learning behaviour. In this study we linked 151 modules and 111.256 students with students' behaviour (<400 million minutes of online behaviour), satisfaction and performance at the Open University UK using multiple regression models. Our findings strongly indicate the importance of learning design in predicting and understanding Virtual Learning Environment behaviour and performance of students in blended and online environments. In line with proponents of social learning theories, our primary predictor for academic retention was the time learners spent on communication activities, controlling for various institutional and disciplinary factors. Where possible, appropriate and well designed communication tasks that align with the learning objectives of the course may be a way forward to enhance academic retention.
The power of learning analytics to measure learning gains: an OU, Surrey and ...Bart Rienties
Learning gains has increasingly become apparent within the HE literature, gained traction in government policies in the UK, and are at the heart of Teaching Excellence Framework (TFL). As such, this raises a question to what extent teaching and learning environment can actually predict students’ learning gains using principles of learning analytics. In this presentation, which is joined work with University of Surrey and Oxford Brookes, I will focus on some preliminary findings based upon developing and testing an Affective-Behaviour-Cognition learning gains model using longitudinal approach. The main aim of the research is to examine whether learning gains occur on all three levels of Affective-Behaviour-Cognition model and whether any particular student or course characteristics can predict learning gains or lack of learning and dropout. For more info, see https://abclearninggains.com/
Learning design meets learning analytics: Dr Bart Rienties, Open UniversityBart Rienties
8th UK Learning Analytics Network Meeting, The Open University, 2nd November 2016
1) The power of 151 Learning Designs on 113K+ students at the OU?
2) How can we use learning design to empower teachers?
3) How can Early Alert Systems improve Student Engagement and Academic Success? (Amara Atif, Macquarie University)
4) What evidence is there that learning design makes a difference over time and how students engage?
Learning design meets learning analytics: Dr Bart Rienties, Open University
Similar to TALIS Presentation: The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules
Drivers of higher education institutions’ visibility: a study of UK HEIs soci...Kim Holmberg
Social media is increasingly used in higher education settings by researchers, students and institutions. Whether it is researchers conversing with other researchers, or universities seeking to communicate to a wider audience, social media platforms serve as a tools for users to communicate and increase visibility. Scholarly communication in social media and investigations about social media metrics is of increasing interest for scientometric researchers, and to the emergence of altmetrics. Less understood is the role of organizational characteristics in garnering social media visibility, through for instance liking and following mechanisms. In this study we aim to contribute to the understanding of the effect of specific social media use by investigating higher education institutions’ presence on Twitter. We investigate the possible connections between followers on Twitter and the use of Twitter and the organizational characteristics of the HEIs. We find that HEIs’ social media visibility on Twitter are only partly explained by social media use and that organizational characteristics also play a role in garnering these followers. Although, there is an advantage in garnering followers for those first adopters of Twitter. These findings emphasize the importance of considering a range of factors to understand impact online for organizations and HEIs in particular.
Track 02 - Educational innovation
Authors: José Carlos Sánchez Prieto, Susana Olmos Migueláñez and Francisco José García-Peñalvo
https://www.youtube.com/watch?v=rP98kYJZyp0&list=PLboNOuyyzZ879QIq5OTq3y3qE62GN4Api&index=5
ASCILITE Webinar: A review of five years of implementation and research in al...Bart Rienties
Date and time: Wednesday 20 September 2017 at 5pm AEST
Abstract: The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, we have been unpacking the complexity of instructional practices, as well as providing empirical evidence of how learning designs influence student behaviour, satisfaction, and performance. This seminar will discuss the implementation of learning design at the OU in the last 5 years, and reviews empirical evidence from several studies that have linked learning design with learning analytics. Recommendations are put forward to support future adoptions of the learning design approach, and potential research trajectories.
https://ascilite.org/get-involved/sigs/learning-analytics-sig/
www.bartrienties.nl
Learning outcome,Task and Topic analysis,Sequencing and chunking.Make a session plan( introduction, body, conclusion).
Linking of Learning Outcomes with Teaching, Learning Activities and Assessment.
Effect of Makerspace Professional Development Activities on Elementary & Midd...STEAM Learning Lab
Dissertation on the Effect of Makerspace Professional Development Activities on Elementary & Middle School Educator Perceptions of Integrating Technologies with STEM (science, technology, engineering, mathematics)
Personalization is an alternative to improve the learning process for an e-Learning environment. It is a useful strategy to adjust the student' needs based on their characteristics to make learning more effectively. In this study, we propose the step-function approach for personalization in e-learning. It provides the students with adopting the knowledge-ability factor (Novice, Average, or Good category) that matches with their learning materials levels (Level1, Level2, or Level3). The approach implemented into an e-learning which called SCELE-PDE and used as the experimental group in two stages with different scenarios. In the first, without a step-function approach, but the SCELE-PDE can identify an initial of student's ability to knowledge category. The second stage has used the approach to providing students with personalization in e-Learning to adapt learning material based on a knowledge category. As a result, the step-function approach has successfully to improve the student performance in the learning process during the course. Thus, the approach has shown an increase in the level of students’ knowledge. So, it can be used as a guide when designing an e-learning personalization for students to enhance learning and achievement.
International Congress of Coaching Psychology 2014 (Kiuchi & Aoki)Keita Kiuchi
Slides presented at an International congress of coaching psychology 2014 held by the Special Group in Coaching Psychology in the British Psychological Society
A relevant literature review suggests that today’s children are increasingly immersing themselves in ubiquitous technologies, including interactive media and digital games. Therefore, this research uses valid measures to investigate the primary school students’ motivations toward playing educational games, at home and at school. The study was carried out amongst year-3 students in a small European state. The findings reported that there were strong correlations between the students’ perceived usefulness of the educational games and their behavioral intention to use them for their learning. The results also indicated that there was no significant relationship between the perceived ease of game-play and the children’s enjoyment in engaging with the school’s digital games. To the best of our knowledge, there is no other study in academia that has explored the children’s technology acceptance, normative pressures and their intrinsic motivations to use digital learning games in the context of primary education. Therefore, this contribution opens future research avenues, as this study can be replicated in other contexts.
Similar to TALIS Presentation: The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules (20)
Applying and translating learning design and analytics approaches in your ins...Bart Rienties
This interactive workshop delivered by the University of Zagreb, Faculty of Organisation and Informatics (UZ) and the Open University UK (OU) will build on two large-scale implementations of learning design and learning analytics, and how you could potentially implement similar approaches in your institution. The OU has been implementing learning design for nearly 20 years as a structured design, specification, and review process for blended and online courses. The learning design is focused on "what students do" as part of their learning, rather than on "what teachers do" or on what will be taught.
Building on this work, UZ has recently developed the Balanced Design Planning (BDP) tool specifically for educators working in hybrid and blended contexts. The tool is more focused on intended learning outcomes and automated learning analytics and is currently being developed, tested, evaluated and implemented with 1000+ practitioners from dozens of institutions in 20+ countries as part of four European projects (eDesk, Teach4EDU, RAPIDE, iLED), and is publicly available for other institutions to use for free. It has been shown by studies conducted by OU and UZ that when these learning design (LD) approaches are used, they help educators to make real-time informed decisions based on learning analytics (LA) and improve the predictive modelling of student behaviour.
Attendees should bring their laptop for this workshop session.
Bart Rienties, Professor in Learning Analytics, Institute of Education Technology, The Open University
Keynote Presentation: Implementing learning analytics and learning design at ...Bart Rienties
The University of the Roller Coaster
How can Higher Education function in a world struggling to save itself from climate change, pandemics and war? How can it drive innovation and shape the future as the pace of technological change constantly increases? How can it re-invent itself to respond imaginatively to the new challenges facing humanity?
We are living in an uncertain, unpredictable world with no “back to normal” any more. So, how can we re-imagine higher education when nothing can be taken for granted? What kind of technologies can help universities to adapt? What lessons can we learn from recent successes and failures? What 'best practice' examples point the way into the future? How can we shape the development of institutions, so that they are neither “ivory towers” nor “competence factories"? How can we encourage future-oriented universities in which both pedagogy and research are fit for the challenges ahead?
In the Academic Plenary, our experts will examine the threats and opportunities facing higher education today and ask how we can design new approaches that prepare staff and students to thrive in the University of the Roller Coaster.
Edutech_Europe Keynote Presentation: Implementing learning analytics and lear...Bart Rienties
This keynote will help you:
-Understand where to start with learning analytics
-Understand how to effectively support your staff to use data
-Critically review whether learning analytics is something for your organisation
https://www.terrapinn.com/exhibition/edutech-europe/speaker-bart-RIENTIES.stm
How can you use learning analytics in your own research and practice: an intr...Bart Rienties
While many “brick-and-mortar” universities had to rapidly shift online provision during the pandemic, a range of online and distance learning universities have been teaching in blended and online formats for years. Obviously with every single click potentially interesting data might become available about how and perhaps why learners are engaging with learning materials and activities. A blossoming field of learning analytics has emerged since 2011 trying to make sense of these increased data flows. The Open University UK (OU) has been trailblazing innovative learning across the globe for 50 years. Since 2014 the OU has gradually moved from small-scale experimentation to large-scale adoption of learning analytics throughout all 400+ modules and qualifications available within the OU for its 170.000+ online learners.
This keynote will explore how you as researcher, practitioner, and/or policy maker could start to use learning analytics to better understand your educational practice. Using examples from small-scale experiments and large-scale adoptions of predictive learning analytics I will explore together with EDEN RW participants which approaches and methods in learning analytics might be useful to consider. No prior knowledge or experience of learning analytics is expected, and join me on a journey of how you could potentially use data from your learners and teachers to further improve and finetune your blended and online provision.
Dr. Bart Rienties is Professor of Learning Analytics and programme lead of the learning analytics and learning design research programme at the Institute of Educational Technology at the Open University UK. He leads a group of academics who provide university-wide learning analytics and learning design solutions and conduct evidence-based research of how students and professionals learn. His primary research interests are focussed on Learning Analytics, Professional Development, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects, and has received a range of awards for his educational innovation projects. He has published over 285 academic outputs, and is the 2nd most published author on Networks in Education in period 1969-2020 (Saqr et al. 2022), the 3rd most cited author on higher education internationalisation in Asia in the period 2013-2018 (Can & Hou, 2021), the 4th most cited author and contributor in Learning Analytics in the period 2011-2018 (Adeniji, 2019), the 5th most published author on internationalisation in the period 1900-2018 (Jing et al. 2020) and the 7th most published author on social network analysis in social sciences in the period 1999-2018 (Su et al. 2020), and the 14th most published author on educational technology in the period 2015-2018 (West & Bodily, 2020).
SAAIR: Implementing learning analytics at scale in an online world: lessons l...Bart Rienties
Workshop objectives:
Explore how institutions like Open University UK have implemented learning analytics at scale. Workshop activities:
Presentation from the facilitator and interactive with questions via pollev, chat, and Zoom. Facilitator biography:
Dr. Bart Rienties is Professor of Learning Analytics and programme lead of the learning analytics and learning design research programme at the Institute of Educational Technology at the Open University UK. He leads a group of academics who provide university-wide learning analytics and learning design solutions and conduct evidence-based research of how students and professionals learn. As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Professional Development, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects, and has received a range of awards for his educational innovation projects. He has published over 250 academic outputs, and is the 4th most cited author and contributor in Learning Analytics in the period 2011-2018 (Adeniji, 2019), the 5th most published author on internationalisation in the period 1900-2018 (Jing et al. 2020) and the 3rd most cited author on higher education internationalisation in Asia in the period 2013-2018 (Can & Hou, 2021), the 7th most published author on social network analysis in social sciences in the period 1999-2018 (Su et al. 2020), and the 14th most published author on educational technology in the period 2015-2018 (West & Bodily, 2020). More info at https://iet.open.ac.uk/people/bart.rienties
OU/Leverhulme Open World Learning: Knowledge Exchange and Book Launch Event p...Bart Rienties
This online event will be a showcase of leading research in the field of open learning, conducted by Doctoral Scholars of The Open University and Leverhulme Trust’s Open World Learning programme, whose work is being recognised with the launch of a new open-access Open World Learning Book.
The event will feature an opening panel discussion on the achievements of our Doctoral Scholars, a collection of themed break-out sessions where scholars will share their research studies and their social impacts, and close with a roundtable where our scholars will consider the future of open learning.
Learning in the 21st century is undergoing both subtle and radical transformation due to the impact of digital, innovative, network technologies. Open learning provides unprecedented access to educational information, providing support to learners worldwide. However, it is not the technologies themselves that represent the biggest change, but the opportunities for access to formal and informal learning.
The Open World Learning programme has been funded by the Leverhulme Trust and The Open University to provide 18 Scholars the opportunity to identify changes in open learning which may exclude, rather than include those who would most benefit. Despite technological advancements, the main challenges to open learning are access-related. Our Open World Learning Scholars have been researching the barriers to access for those whose experiences open learning can benefit most and addressing issues where possible.
Hosted by Professor Bart Rienties, Programme Lead of the Open World Learning programme at the OU's Institute of Educational Technology, this two-hour event will provide a knowledge exchange platform to learn from our Open World Learning Doctoral Scholars and celebrate their exceptional achievements with the Open World Learning Book Launch.
We hope you join us and register to attend our free event. Follow us on the IETatOU Twitter and visit the IET website where a series of digital and social content will be shared highlighting the work of our Open World Learning scholars.
Visit us here: https://iet.open.ac.uk | https://twitter.com/ietatou
Education 4.0 and Computer Science: A European perspectiveBart Rienties
This systematic literature review aimed at identifying the pedagogical approaches, aligned with Education 4.0, used to support teaching computer science courses with undergraduate and graduate students in Europe. A three-step coding process was conducted to identify and analyse 20 papers. Quantitative and qualitative analysis of the selected papers revealed a three-cluster solution with common characteristics that could be used to describe those pedagogical approaches. The review also showed that the term Education 4.0 is still relatively new and has not been conceptualised in terms of computer science courses, although the characteristics of Education 4.0 are visible throughout the pedagogical approaches.
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The Open University UK (OU) has been implementing learning analytics and learning design on a large scale since 2012. With its 170+ students and 4000+ teaching staff, the OU has been at the forefront of testing, implementing, and evaluating the impact of learning analytics and learning design on students outcome and retention. A range of reviews and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics and learning design in the world. However, despite the large uptake of learning analytics at the OU there are a range of complex issues in terms of buy-in from staff, data infrastructures, ethics and privacy, student engagement, and perhaps most importantly how to make sense of big and small data in a complex organisation like the OU. During his talk Bart will be presenting on the implementation and learnings.
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The Open University (OU) was an early adopter of learning analytics, and after six years has had the opportunity to reflect on the impact of large scale adoption across the institution.
Has there been an impact on student retention/progress/completion?
How are the positives (or negatives) reflected in student satisfaction surveys?
What worked, what didn't, and with this benefit of hindsight what is, or should be, next?
What have we learned from 6 years of implementing learning analytics amongst ...Bart Rienties
By Professor Bart Rienties, Head of Academic Professional Development, Institute of Educational Technology, The Open University, UK
Abstract
The Open University UK (OU) has been implementing learning analytics since 2014, starting with one or two modules to its current practice of large-scale implementation across all its 400+ modules and 170.000+ students and 4000+ teaching staff. While a range of reviews (e.g., Adenij, 2019) and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics in the world, behind the flashy publications and practitioner outputs there are a range of complex issues in terms of ethics and privacy, data infrastructures, buy-in from staff, student engagement, and how to make sense of big data in a complex organisation like the OU.
Based upon large-scale big data research we found some interesting tensions in both design and educational theory, such as:
– 69% of engagement by students on a week by week basis is determined by how teachers are designing courses (i.e., learning design and instructional design indeed directly influence behaviour and cognition), but many teachers seem reluctant to change their learning design based upon data of what works and what does not work (e.g., making sense of data, agency);
– How teachers engage with predictive learning analytics (PLA) significantly improves student outcomes, but only a minority of teachers actually use PLA;
– Some disadvantaged groups engage more actively in OU courses, but nonetheless perform lower than non-disadvantaged students.
During this CELDA keynote I would like to share some of my own reflections of how the OU has implemented learning analytics, and how these insights are helping towards a stronger evidence-base for data-informed change. Furthermore, by sharing some of the lessons learned from implementing learning analytics on a large scale I hope to provide some dos and don’ts in terms of how you might consider to use data in your own practice and context.
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https://www.kent.ac.uk/cshe/news-events.html
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There are no prior requirements to join, and everyone is welcome. For those with a technical background you may enjoy this recent paper in PLOS ONE https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233977. For those with a non-technical background, you may enjoy this paper https://journals.sfu.ca/flr/index.php/journal/article/view/348
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
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The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
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• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
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TALIS Presentation: The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules
1. The impact of learning design on
student behaviour, satisfaction and
performance: a cross-institutional
comparison across 151 modules
@DrBartRienties
Reader in Learning Analytics
Lisette Toetenel
Senior manager Learning Design
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12. Assimilative Finding and
handling
information
Communicati
on
Productive Experiential Interactive/
Adaptive
Assessment
Type of
activity
Attending to
information
Searching for
and
processing
information
Discussing
module related
content with at
least one other
person (student
or tutor)
Actively
constructing an
artefact
Applying
learning in a
real-world
setting
Applying
learning in a
simulated
setting
All forms of
assessment,
whether
continuous,
end of
module, or
formative
(assessment
for learning)
Examples of
activity
Read, Watch,
Listen, Think
about,
Access,
Observe,
Review, Study
List, Analyse,
Collate, Plot,
Find,
Discover,
Access, Use,
Gather, Order,
Classify,
Select,
Assess,
Manipulate
Communicate,
Debate,
Discuss, Argue,
Share, Report,
Collaborate,
Present,
Describe,
Question
Create, Build,
Make, Design,
Construct,
Contribute,
Complete,
Produce, Write,
Draw, Refine,
Compose,
Synthesise,
Remix
Practice,
Apply, Mimic,
Experience,
Explore,
Investigate,
Perform,
Engage
Explore,
Experiment,
Trial, Improve,
Model,
Simulate
Write,
Present,
Report,
Demonstrate,
Critique
13.
14. Method – data sets
• Combination of four different data sets:
• learning design data (189 modules mapped,
276 module implementations included)
• student feedback data (140)
• VLE data (141 modules)
• Academic Performance (151)
• Data sets merged and cleaned
• 111,256 students undertook these modules
15. Toetenel, L. & Rienties, B. (2016). Analysing 157 Learning Designs using Learning Analytic approaches as a means to evaluate the impact of pedagogical
decision-making. British Journal of Educational Technology.
16. Toetenel, L. & Rienties, B. (2016). Analysing 157 Learning Designs using Learning Analytic approaches as a means to evaluate the impact of pedagogical
decision-making. British Journal of Educational Technology.
17. Constructivist
Learning Design
Assessment
Learning Design
Productive
Learning Design
Socio-construct.
Learning Design
VLE Engagement
Student
Satisfaction
Student
retention
Learning Design
151 modules
Week 1 Week 2 Week30
+
Disciplines Levels
Size module
Rienties, B., Toetenel, L., (2016). The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across
151 modules. Computers in Human Behavior, 60 (2016), 333-341
24. Model 1 Model 2 Model 3
Level0 -.279** -.291** -.116
Level1 -.341* -.352* -.067
Level2 .221* .229* .275**
Level3 .128 .130 .139
Year of implementation .048 .049 .090
Faculty 1 -.205* -.211* -.196*
Faculty 2 -.022 -.020 -.228**
Faculty 3 -.206* -.210* -.308**
Faculty other .216 .214 .024
Size of module .210* .209* .242**
Learner satisfaction (SEAM) -.040 .103
Finding information .147
Communication .393**
Productive .135
Experiential .353**
Interactive -.081
Assessment .076
R-sq adj 18% 18% 40%
n = 140, * p < .05, ** p < .01
Table 3 Regression model of LMS engagement predicted by institutional, satisfaction and learning design analytics
• Level of study predict VLE
engagement
• Faculties have different VLE
engagement
• Learning design
(communication & experiential)
predict VLE engagement (with
22% unique variance
explained)
25. Model 1 Model 2 Model 3
Level0 .284** .304** .351**
Level1 .259 .243 .265
Level2 -.211 -.197 -.212
Level3 -.035 -.029 -.018
Year of
implementation .028 -.071 -.059
Faculty 1 .149 .188 .213*
Faculty 2 -.039 .029 .045
Faculty 3 .090 .188 .236*
Faculty other .046 .077 .051
Size of module .016 -.049 -.071
Finding information -.270** -.294**
Communication .005 .050
Productive -.243** -.274**
Experiential -.111 -.105
Interactive .173* .221*
Assessment -.208* -.221*
LMS engagement .117
R-sq adj 20% 30% 31%
n = 150 (Model 1-2), 140 (Model 3), * p < .05, ** p < .01
Table 4 Regression model of learner satisfaction predicted by institutional and learning design analytics
• Level of study predict
satisfaction
• Learning design (finding info,
productive, assessment)
negatively predict satisfaction
• Interactive learning design
positively predicts satisfaction
• VLE engagement and
satisfaction unrelated
26. Model 1 Model 2 Model 3
Level0 -.142 -.147 .005
Level1 -.227 -.236 .017
Level2 -.134 -.170 -.004
Level3 .059 -.059 .215
Year of implementation -.191** -.152* -.151*
Faculty 1 .355** .374** .360**
Faculty 2 -.033 -.032 -.189*
Faculty 3 .095 .113 .069
Faculty other .129 .156 .034
Size of module -.298** -.285** -.239**
Learner satisfaction (SEAM) -.082 -.058
LMS Engagement -.070 -.190*
Finding information -.154
Communication .500**
Productive .133
Experiential .008
Interactive -.049
Assessment .063
R-sq adj 30% 30% 36%
n = 150 (Model 1-2), 140 (Model 3), * p < .05, ** p < .01
Table 5 Regression model of learning performance predicted by institutional, satisfaction and learning design analytics
• Size of module and discipline
predict completion
• Satisfaction unrelated to
completion
• Learning design
(communication) predicts
completion
27. Constructivist
Learning Design
Assessment
Learning Design
Productive
Learning Design
Socio-construct.
Learning Design
VLE Engagement
Student
Satisfaction
Student
retention
150+ modules
Week 1 Week 2 Week30
+
Rienties, B., Toetenel, L., (2016). The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across
151 modules. Computers in Human Behavior, 60 (2016), 333-341
Communication
28.
29.
30. Toetenel, L., Rienties, B. (2016) Learning Design – creative design to visualise learning activities. Open Learning.
31.
32. The impact of learning design on
student behaviour, satisfaction and
performance: a cross-institutional
comparison across 151 modules
@DrBartRienties
Reader in Learning Analytics
Lisette Toetenel
Senior manager Learning Design
Editor's Notes
Learning Design Team has mapped 100+ modules
For each module, the learning design team together with module chairs create activity charts of what kind of activities students are expected to do in a week.
For each module, detailed information is available about the design philosophy, support materials, etc.
Explain seven categories
5131 students responded – 28%, between 18-76%
Cluster analysis of 40 modules (>19k students) indicate that module teams design four different types of modules: constructivist, assessment driven, balanced, or socio-constructivist. The LAK paper by Rienties and colleagues indicates that VLE engagement is higher in modules with socio-constructivist or balanced variety learning designs, and lower for constructivist designs. In terms of learning outcomes, students rate constructivist modules higher, and socio-constructivist modules lower. However, in terms of student retention (% of students passed) constructivist modules have lower retention, while socio-constructivist have higher. Thus, learning design strongly influences behaviour, experience and performance. (and we believe we are the first to have mapped this with such a large cohort).
Cluster analysis of 40 modules (>19k students) indicate that module teams design four different types of modules: constructivist, assessment driven, balanced, or socio-constructivist. The LAK paper by Rienties and colleagues indicates that VLE engagement is higher in modules with socio-constructivist or balanced variety learning designs, and lower for constructivist designs. In terms of learning outcomes, students rate constructivist modules higher, and socio-constructivist modules lower. However, in terms of student retention (% of students passed) constructivist modules have lower retention, while socio-constructivist have higher. Thus, learning design strongly influences behaviour, experience and performance. (and we believe we are the first to have mapped this with such a large cohort).