Comparing departmental ‘baseline’ and ‘opt-in’ strategies for e-learning adoption

across an institutionWhich works best?

Richard WalkerE-Learning Development Team

University of York

ALT-C 2009

Transforming pedagogic practice:Institutional & sector trends

YorkLate adopter; e-learning infrastructure fully rolled out; variable academic engagement across departments

HE SectorTEL – 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 (2007-08).– 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.

http://www.ucisa.ac.uk/groups/ssg/surveys.aspx

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 2008. Retrieved July 16, 2009 from

http://www.dius.gov.uk/higher_education/shape_and_structure/he_debate/~/media/publications/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 81-95.

Elgort, I. 2005. E-learning adoption: Bridging the chasm. Proceedings ascilite Brisbane, 2005.

http://www.ascilite.org.au/conferences/brisbane05/blogs/proceedings/20_Elgort.pdf

Sharpe, R., G.Benfield, and R. Francis. 2006. 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.