Comparing departmental ‘baseline’ and ‘opt-in’ strategies for e-learning adoption across an institution Which works best? Richard Walker E-Learning Development Team University of York ALT-C 2009

Transforming pedagogic practice: Institutional & sector trends York Late adopter; e-learning infrastructure fully rolled out; variable academic engagement across departments HE Sector TEL – increasingly developed institutionally; recognised & underpinned through institutional strategies; but transformative impact on pedagogic practice not yet realized (Cooke, 2008) Key challenge : staff skills (OBHE survey, 2006; UCISA TEL survey, 2008)

Managing adoption: York’s approach Top Down Bottom Up Strategic planning (DeFreitas & Oliver, 2005) Centrally managed pilots & project funding Evaluation reviews… informing training & user guidelines Departmental strategy development - ‘owned, local & relevant’ (Sharpe et al., 2006) Departmental control over pace of adoption (mature/developing/pilot models) Delegated training, admin & quality assurance Departmental champions oversee long-term collaboration with central services

Baseline adoption strategies  Baseline department 1 –Social Sciences – full-time / campus based students –100% coverage of modules –Minimum requirement: lecture notes & course docs –Policy predates adoption of University VLE ( ). –Devolved training & support model Roger’s diffusion model (1995) Baseline expectation will ensure higher proportion of staff / students adopt TEL; progressing take-up to include late adopters & laggards.  Baseline department 2 –Social Sciences – mix of full & part time / distance & campus based students –Engagement in pilot phase –100% coverage of modules –Minimum requirement: course info, assessment details, reading list & discussion board –Devolved training & support model: All staff must complete ‘Getting Started’ training

Opt-in strategies  Opt-in department 1 –Science – full-time / campus based students –Engagement in pilot phase (establishment of blended models) –Wide uptake of TEL across taught courses, but not comprehensive –Wish for uptake across department, but not prescriptive Zemsky’s e-learning adoption cycles model (2004): Staff progressing at different speeds through cycles in adoption of TEL and innovation in pedagogic practice.  Opt-in department 2 –Arts / Humanities – full-time / campus based students –Engagement in pilot phase (& legacy use of alternative platform) –Wide uptake across 1st / 2nd year courses –No policy – although plans for VLE usage tied to curriculum redevelopment.

Tracking adoption trends  Focusing on:  Staff & student confidence ratings for e-tools  Range of tools employed  Perceived contribution of online component to learning Annual student survey (…2008 / 2009) Staff survey & strategic review (2008)  Interest in:  Level & depth of engagement with e- tools (pedagogic relevance)  Evolution & transformation of pedagogic practice

B1 Findings for ‘Baseline’ Departments  High confidence for:  Accessing content  Library resources  Assignment submission & quizzes Confidence ratings  Low confidence for:  collaborative & interactive tools B2  High confidence for:  Content  Library resources  Quizzes  Low confidence for:  Collaborative interactive & group tools.  Accessing content Tools employed  Library resources  Assignment submission & quizzes

B1 Findings for ‘Baseline’ Departments  (80% agreement)  supporting access to course resources & flexible personal study: Contribution to learning  ‘allows me to read up on any classes I have missed’ (B2)  but tools ‘not used to full potential’ (B1) B2  (81% agreement)  inconsistent levels of engagement by staff:  ‘more frequent updates to VLE by course facilitators’ (B2)  ‘not all modules have past examples (or enough) or papers’ (B2)

O1 Findings for ‘Opt-in’ Departments  High confidence for:  Accessing content  Self-assessment  Discussion tools Confidence ratings  Low confidence for:  collaborative tools O2  High confidence for:  Content  Self-assessment quizzes  Group-tools (wiki)  Low confidence for:  Assignment submission  Access to content  Assignment submission & Quizzes  Discussion forums & Collaborative tools Tools employed O1  feedback on student work O2

O1 Findings for ‘Opt-in’ Departments  (68% agreement)  supporting flexible personal study, self-assessment but inconsistent use of tools, by staff: Contribution to learning  ‘restricted / inconsistent usage by teaching staff’ (O1)  “it isn’t used enough by most lecturers” (O2) O2  (90% agreement)  requirement for greater use of self- assessment & multimedia resources

Baseline vs. Opt-in strategies: Which works best? Baseline  ‘E’-learning component – highly complementary to class-based learning  Evidenced across a range of courses (mature adoption) – coherence & consistency.  But limited in terms of range of tools / approaches employed.  Drivers for innovation - moving beyond ‘surface’ approaches to e-learning? “I have been forced (to do) it and have found it a complete waste of time.” “Little change since I initially learned how to upload materials and make announcements.”

Baseline vs. Opt-in strategies: Which works best? Opt-in  More critical reception of e-learning component, reflecting restricted range of modules employing e- tools (variable coverage) “Broader use across the department would help as students tend to dip in and out on specific modules only.”  But wider range of blended approaches in evidence.  Students pressing for wider take-up – coherence in learning experience.

Discussion Points 1. Can usage targets stimulate pedagogic innovation? 2. Are rapid roll-outs effective? 3. Is student pressure a force for change? 4. How do we effect cultural change in academic practice? No direct relationship between minimum levels of engagement & direct enhancement to teaching & learning in terms of the way that staff “re- engineer teaching and learning activities to take full and optimal advantage of the new technology” (Zemsky & Massy, 2004). Rapid roll-outs (migration of course materials) may trivialize course design, encouraging surface approaches to e-learning (Elgort, 2005). Student pressure may facilitate the rate of adoption of e-learning at the expense of its quality (Elgort 2005). Consumerism vs. active learning. By addressing technological & pedagogic planes through staff development (UCISA TEL Survey), challenging conceptions about teaching and learning (Elgort, 2005).

References Becker, R. and Jokivirta, L. (2007) Online Learning in Universities: Selected Data from the 2006 Observatory Survey. The Observatory on borderless higher education (OBHE). Browne, T., Hewitt, R., Jenkins, M. & Walker, R. (2008). ‘2008 survey of Technology Enhanced Learning For Higher Education in the UK’. A JISC/UCISA funded survey. Cooke, R. (2008). On-line Innovation in Higher Education. Submission to the Rt Hon John Denham MP. Secretary of State for Innovation, Universities and Skills, 8 October Retrieved July 16, 2009 from lications/S/Summary-eLearning-Cooke DeFreitas, S. & Oliver, M. (2005), Does E-Learning Policy Drive Change in Higher Education?: A Case Study Relating Models of Organisational Change to E-Learning Implementation. Journal of Higher Education Policy and Management. 7: 1, pp Elgort, I E-learning adoption: Bridging the chasm. Proceedings ascilite Brisbane, Sharpe, R., G.Benfield, and R. Francis Implementing a university e-learning strategy: levers for change within academic schools. ALT-J, Research in Learning Technology 14: 135 – 51. Zemsky, R. & Massy, W. (2004). Thwarted innovation: What happened to e-learning and why. The Learning Alliance at the University of Pennsylvania.