SERA 09

Presentation to the SERA 09 conference on GLOW and raising attainment

Education Technology

GLOW – Did the lights stay on ? A quantitative and qualitative 2 year study into raising attainment using ICT SERA 09

BACKGROUND… ,[object Object],[object Object],[object Object],[object Object],[object Object]

GLOW lessons… ,[object Object],[object Object],[object Object],[object Object],[object Object]

Attainment rise… ,[object Object],[object Object]

(The significant p values are indicated * in the table) Teacher differences on topic 3 (April 2008) .805766 .003558* .001750* 4 .805766 .056739 .034660* 3 .003558* .056739 .998774 2 .001750* .034660* .998774 1 81.20000 76.34722 62.01471 61.6667 (Mean) 4 3 2 1 Class

Continuation to May 2009 ( p-Values in red are statistically significant) 0.223 0.000 0.000 4 0.223 0.007 0.000 3 0.000 0.007 0.004 2 0.000 0.000 0.004 1 71.22 66.50 59.24 51.78 Mean 4 3 2 1 Class

Qualitative measures… ,[object Object],[object Object],[object Object]

Amount of Time Spent On Task (TOT) and Time Spent Off Task (TOffT) Table 9 Time On Task (TOT) and Time Off Task (TOffT) * With a p-value of 0.006 (p<0.05), we can state that there is a significant difference in the amount of TOT between Non Glow and Glow lessons. 4.0 (2.4-4.8) 38.4 (17.0-45.7) 29 (20.4-55.0) Median (Range) 0.004* 0.006* 0.867 3.8 (1.0) 33.5 (11.2) 33.8 (11.8) Mean (SD) TOffT 86.9 (85.5-94.0) 55.5 (41.4-65.0) 58 (30.0-63.3) Median (Range) 0.004* 0.006* 0.397 88.3 (3.9) 55.2 (8.3) 52.8 (12.1) Mean (SD) TOT 2 vs. 3 p 1 vs. 3 p 1 vs. 2 p TI Glow NTI Non Glow TI Non Glow 3 2 1

With a p-value of 0.012 (p<0.05), we can state that there is a significant difference in the amount of P-P TR between TI Non Glow and TI Glow lessons 0.0 (0.0-0.0) 0.0 (0.0-30.0) 0.0 (0.0-5.3) Median (Range) 0.570 0.527 1.000 0.0 (0.0) 4.4 (10.5) 1.3 (2.3) Mean (SD) T-P NTR 66.2 (44.4-84.2) 41.7 (5.0-58.8) 36.8 (20.8-63.6) Median (Range) 0.368 0.164 0.955 65.3 (19.8) 38.1 (18.1) 37.1 (14.4) Mean (SD) T-P TR 0.0 (0.0-0.0) 0.0 (0.0-4.4) 0.0 (0.0-1.3) Median (Range) 0.570 0.788 0.694 0.0 (0.0) 0.9 (1.7) 0.2 (0.5) Mean (SD) P-T NTR 3.5 (2.9-7.4) 12.8 (0.0-31.7) 13.5 (3.2-20.0) Median (Range) 0.283 0.109 1.000 4.3 (2.1) 13.2 (10.8) 12.5 (6.3) Mean (SD) P-T TR 3.4 (1.3-4.9) 15.7 (0.0-43.0) 14.0 (4.0-29.1) Median (Range) 0.154 0.024* 0.955 3.2 (1.6) 19 (15.6) 16.7 (8.7) Mean (SD) P-P NTR 19.3 (12.4-22.7) 1.5 (0.0-8.0) 0.0 (0.0-20.0) Median (Range) 0.004* 0.012* 0.955 18.4 (4.5) 3.1 (3.6) 5.0 (7.7) Mean (SD) P-P TR p p p TI Glow NTI Non-Glow TI Non Glow 2 vs. 3 1 vs. 3 1 vs. 2 3 2 1

Initial conclusions.. ,[object Object],[object Object]

Contd… ,[object Object],[object Object],[object Object],[object Object]

REAL LEARNING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

(adapted from Prof. Stephen Heppell 2006 conference presentation http://rubble.heppell.net/creativeJISC/default.html)

Looking to the future.. ,[object Object]

From http://olliebray.typepad.com/olliebraycom/2009/08/21st-century-teaching-tools-south-lanarkshire.html

The future… ,[object Object],[object Object],[object Object],[object Object],[object Object]

“ Stop talking, cut the spin and hype and start some serious evaluation”

A culture of secrecy has existed around GLOW development

GLOW Learn has been ill thought out and is too impersonal. “ Teaching by machines” went out with behaviourism

Moratorium on all further development until… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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.

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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/

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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.

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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.

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Title: Reflections on methodology used in assessing teachers´perceptions of collective efficacy. Working with emerging curriculum areas and action competence Abstract/ Introduction In many countries new curriculum areas are emerging in response to societal changes. This calls for enhanced efficacy and success depends on whether teachers trust themselves and their colleagues to meet new demands. Further, learners are expected to develop agency for change and teachers to develop collective teacher efficacy (CTE). Increased emphasis on competences and global comparative studies…. Children of today may have fewer opportunities to challenge and solve open ended problems. The power concept CTE enables educators to identify their own weaknesses and strengths in relation to the challenges of the 21st century. Research identified four new curriculum areas in the EmergeCTE project where particular demands were made of teachers: sustainability, use of information and communication technology, innovation education and school science in the 21st century and focused on action competence (AC) as the teaching task. Key features of AC individuals are that they are participants capable of being critical actors in democratic processes. The purpose of this symposium is to discuss selected issues from the EmergeCTE research. These include issues in the development of the questionnaires (paper 1), the role of case study in supporting questionnaire development (paper 2) and finally about mining the data and finding the factors (paper 3). The citation (APA style) Svanborg R. Jónsdóttir, Allyson Macdonald, Svanborg R. Jónsdóttir, Svava Pétursdóttir og Sigurbjörg Jóhannesdóttir. (2017, 23. November). Reflections on methodology used in assessing teachers´perceptions of collective efficacy. Working with emerging curriculum areas and action competence. Á SERA Scottish Education Research Association Annual Conference. University of Dundee, Scotland.Retrieved from https://www.slideshare.net/sibba/reflections-on-methodology

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