Diagnostic tool to support transition into study

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A diagnostic tool developed to support Open University students into study. Piloted and tested in 2016. Project led by Dr Sam Smidt, The Open University with Carol Pate. Diagnostic based on a tool developed by Prof Allison Littlejohn's group at Glasgow Caledonian University.

Education

Transitions into OU Study
Psychometric tool to trigger discussion & reflection
Professor Allison Littlejohn (@allisonl)
Academic Director of Digital Innovation
The Open University

Aimed to:
● Improve student retention by reducing drop-out between
registration and module start;
● Set students on the right pathway, thus reducing the number
of Change of Study Intention queries.
To achieve these goals the project strategically focused on student
induction and diagnostics, addressing critical issues in these areas.
How is transition supported?

A process – in two inter-related parts
How is transition supported?
• Part 1: entry into conversation
• Part 2: charting the conversation
3
Game/app
diagnostics
induction
Link to
Facebook?
psychometric
Proactive
contact
choices ‘prep’
Best starting
point
advice

Other tools considered
● ELLI, ELLIMent, CLARA
● These were developed for childrens’ learning
● The OU diagnostic was developed for adults
in work contexts.
4

The diagnostic tool was piloted within DD102, Introduction to Social
Science in 2016 http://www.open.ac.uk/courses/modules/dd102
Tutors were given advice on how to use the outcomes of the diagnostic
to start a conversation with the learner about learning preferences and
future goals.
The diagnostic provided:
• A personalized basis on which the tutor could base a conversation.
• An instant outcome for learners, encouraging reflection on how they
perceive their learning.
• Planning how to improve learning in the future.
Diagnostic based on Fontana, Rosa Pia; Milligan, Colin; Littlejohn,
Allison and Margaryan, Anoush (2015). Measuring self-regulated
learning in the workplace. International Journal of Training and
Development, 19(1) pp. 32–52.
Diagnostic Pilot

Diagnostic Questions
http://www3.open.ac.uk/forms/transitions-feedback/

http://www3.open.ac.uk/forms/transitions-feedback/

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1. A MOOC on data science offered by the University of Washington on Coursera had over 40,000 registered learners from 79 countries. Surveys were collected from 788 learners and 30 follow up interviews were conducted. 2. Lower self-regulated learners (LSRL) were more motivated to complete the course certificate to add to their LinkedIn profiles and prioritize courses offering certificates. Higher self-regulated learners (HSRL) linked their motivations more to improving work performance or personal interest rather than certification. 3. HSRL tended to link their learning goals directly to improving their work, while LSRL focused more on external validation through course completion certificates.

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1. Experts were interviewed to understand knowledge gaps related to capacity strengthening for drug-resistant infections in low and middle-income countries. 2. Seven key themes emerged from the interviews, including the need for multi-sectoral collaboration, functioning laboratory networks, data flow across different systems, trust among professionals, motivated skilled professionals, and learning that facilitates new practices. 3. Experts prioritized nine categories of knowledge and skills for capacity building, including diagnostic stewardship, good laboratory practice, and data interpretation for public health policy. Targeting specific job roles and encouraging collaboration were recommended.

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1. The document discusses learning events to build capacity around antimicrobial resistance (AMR) surveillance in low and middle-income countries (LMICs). 2. In year 1, the objectives were to learn the context, define learning requirements, conduct a learning needs analysis, design and deliver initial learning events, and develop a roadmap for future learning approaches. Country visits identified knowledge and skill gaps in various professional roles. 3. The document outlines a multi-year approach to developing longer-term learning topics, methods, and delivery modes to address AMR issues in LMICs.

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1. The document discusses professional learning and how it connects to work. It examines how professionals learn through formal, informal, and non-formal means and the relationship between learning opportunities, activities, and the organization of work. 2. Case studies on health, energy, and finance professionals are presented to show how their learning relates to addressing issues in their fields and adapting work practices. Interviews and analysis of these cases identify knowledge and skills needs and potential learning events. 3. Key messages are that professional learning is shaped by workplace culture and practice, reorganizing work is needed to better support learning, and learning should help reflect on both knowledge and how work is organized.

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This document summarizes a discussion on professional learning in the workplace and higher education. It examines factors that create an understanding of professional learning, including how professionals learn, case studies of professional learning, and a model to inform future technology-enhanced professional learning. Collaborators from various universities then discussed applying this framework to different sectors such as finance, health, and energy to better understand professional learning challenges and opportunities in those domains.

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1) The document discusses professional learning through examining factors that create an understanding of professional learning such as how professionals learn and case studies. 2) It presents a model to inform future technology-enhanced professional learning consisting of three factors: self-regulated learning, workplace learning opportunities, and workplace learning activities. 3) Case studies on learning in finance professionals, data science MOOCs, health MOOCs, and midwifery MOOCs are discussed to understand motivations and self-regulated learning strategies used.

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Diagnostic tool to support transition into study

1. Transitions into OU Study Psychometric tool to trigger discussion & reflection Professor Allison Littlejohn (@allisonl) Academic Director of Digital Innovation The Open University

2. Aimed to: ● Improve student retention by reducing drop-out between registration and module start; ● Set students on the right pathway, thus reducing the number of Change of Study Intention queries. To achieve these goals the project strategically focused on student induction and diagnostics, addressing critical issues in these areas. How is transition supported?

3. A process – in two inter-related parts How is transition supported? • Part 1: entry into conversation • Part 2: charting the conversation 3 Game/app diagnostics induction Link to Facebook? psychometric Proactive contact choices ‘prep’ Best starting point advice

4. Other tools considered ● ELLI, ELLIMent, CLARA ● These were developed for childrens’ learning ● The OU diagnostic was developed for adults in work contexts. 4

5. The diagnostic tool was piloted within DD102, Introduction to Social Science in 2016 http://www.open.ac.uk/courses/modules/dd102 Tutors were given advice on how to use the outcomes of the diagnostic to start a conversation with the learner about learning preferences and future goals. The diagnostic provided: • A personalized basis on which the tutor could base a conversation. • An instant outcome for learners, encouraging reflection on how they perceive their learning. • Planning how to improve learning in the future. Diagnostic based on Fontana, Rosa Pia; Milligan, Colin; Littlejohn, Allison and Margaryan, Anoush (2015). Measuring self-regulated learning in the workplace. International Journal of Training and Development, 19(1) pp. 32–52. Diagnostic Pilot

6. Diagnostic Questions http://www3.open.ac.uk/forms/transitions-feedback/

7. Diagnostic Questions http://www3.open.ac.uk/forms/transitions-feedback/

9. Diagnostic Questions http://www3.open.ac.uk/forms/transitions-feedback/

10. http://www3.open.ac.uk/forms/transitions-feedback/

11. Data can be presented in different ways

12. Learners who had a conversation were 3-5% more likely to be still studying than those students with similar demographics who were not contacted. (Transitions into OU Study, Final Report, 2016). App helped learners understand their preparedness for study, helps them make choices and gives them a self-generated profile based on psychometric/dispositional elements. Provided a social space, opportunities to share resources partly student generated. The diagnostic uses the learner’s responses to push different suggestions and options for them to explore. Guided proactive support to ensure learners understand their choices, have some concrete suggestions for ‘prep’ and have received advice about the best starting point/study intensity/additional support options. Pilot findings

Editor's Notes

-a tool/game/app that is fun for students, helps them understand their preparedness for study, helps them make choices and gives them a self-generated profile based on psychometric/dispositional elements - FB game? social space, opportunities to share resources around that. – partly student generated, strong elements of induction but removing the structured ‘work through it all’ sense that makes induction often unpopular (Student Experience Research 2012 NUS – ‘first year student experience’). Course induction gets better reviews hence this generic ‘light touch’ but this is still work in progress and we are working with Student services and with OSEP to provide a single version of the truth. The game/app will include elements of semantic analysis, using the learner’s responses to push different suggestions and options for them to explore. – proactive support to ensure learners understand their choices, have some concrete suggestions for ‘prep’ (if desired) and have received advice about the best starting point/study intensity/additional support options etc
-a tool/game/app that is fun for students, helps them understand their preparedness for study, helps them make choices and gives them a self-generated profile based on psychometric/dispositional elements - FB game? social space, opportunities to share resources around that. – partly student generated, strong elements of induction but removing the structured ‘work through it all’ sense that makes induction often unpopular (Student Experience Research 2012 NUS – ‘first year student experience’). Course induction gets better reviews hence this generic ‘light touch’ but this is still work in progress and we are working with Student services and with OSEP to provide a single version of the truth. The game/app will include elements of semantic analysis, using the learner’s responses to push different suggestions and options for them to explore. – proactive support to ensure learners understand their choices, have some concrete suggestions for ‘prep’ (if desired) and have received advice about the best starting point/study intensity/additional support options etc
-a tool/game/app that is fun for students, helps them understand their preparedness for study, helps them make choices and gives them a self-generated profile based on psychometric/dispositional elements - FB game? social space, opportunities to share resources around that. – partly student generated, strong elements of induction but removing the structured ‘work through it all’ sense that makes induction often unpopular (Student Experience Research 2012 NUS – ‘first year student experience’). Course induction gets better reviews hence this generic ‘light touch’ but this is still work in progress and we are working with Student services and with OSEP to provide a single version of the truth. The game/app will include elements of semantic analysis, using the learner’s responses to push different suggestions and options for them to explore. – proactive support to ensure learners understand their choices, have some concrete suggestions for ‘prep’ (if desired) and have received advice about the best starting point/study intensity/additional support options etc

Diagnostic tool to support transition into study

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Editor's Notes