Why?

“Students must be effective self-assessors; to be anything less is to be dangerously ill-prepared to cope with change” (Boud 2000, p. 160).

As part of the development of both sound academic practice and lifelong learning skills, the ability to make self-evaluative assessment of competencies, attributes and learning preferences/needs are vital. Students need to have a clear understanding of these, and to develop these, as-well-as the confidence to apply them. Students need opportunities beyond formal assessment and feedback to reflect on the competencies required and to recognise their attributes, strengths and weaknesses and preferences in order to identify and plan for areas of development.

Development of these skills are key to independent enquiry and self-regulation.

The development of structured self-assessments can be used to allow students to reflect upon their own levels of confidence linked to key competencies, attributes and preferences; direct them to a variety of targeted self-access resources, including support, relevant to their levels of confidence; and create space for them to identify their own development plans.

One other key benefit is that the student response data can also be used to inform future course/resource content development, including requirements around preferences/needs.

What is it?

The confidence-based self-assessment diagnostic tool provides a mechanism for staff to create bespoke diagnostics, allowing students to align their levels of confidence around the skills, needs, preferences and competencies that they will need on their course (and/or placement or future employment) and to (self) determine a plan of action to develop these skills and competencies along-side their studies.

It also provides a useful method to stimulate a formative exchange between the students and their tutors.

The diagnostic has been built to accommodate up to six categories, each containing eight confidence-based questions (I can…). The students choose a response indicating - high confidence or reasonable confidence or low confidence or don’t know/no experience. All questions are randomised and category information is not provided until the response page. Based upon the responses given the students are presented with recommendations linked to practical suggestions in url links (to workshops, practicums, online resources & courses, et cetera). Students then set their own developmental goals based upon these recommendations.

See Create a Diagnostic for further information of the customisable fields.

While the student responses are anonymised, each user generates an individual ID. The student data (question responses and areas for development) is available in a CSV format for analysis.

How might I use it?

Students need to have a clear understanding of skills required for their studies, research and/or future employment. By building self-assessments linked to these skills, we can help students to identify their own tailored and unique areas for development and to link these to specific support and resources.

Examples of areas where a diagnostic might be developed
  • supporting students at Course level to 'signpost' the skills and attributes that they will need and linking them to support and resources
  • supporting students using labs to 'signpost ' support and resources in areas such as equipment calibration, health and safety, rules and regulations
  • supporting students with non-academic needs to 'signpost' support and resources
  • supporting students and staff to 'signpost' resources and support around digital capabilities

What are the pros & cons?

Pros
  • built with accessibility in mind
  • built to be user-friendly
  • no text editor required to create content
  • simple Content Management Template design
  • device agnostic design - makes it possible to view content on different devices (such as tablets and smartphones)
  • category and question banks are stored openly, for collaboration or sharing
  • URL based content means it can be deployed via any web-based tool - such as Moodle, email, social media
  • diagnostics can be reused
  • accessible only via SSO (Single Sign-on)
  • student data anonymised
  • response data available for analysis
Cons
  • accessible only via SSO (Single Sign-on)
  • student data anonymised
  • the Diagnostic Tool is only as useful as the recommendations and resources provided

Case study

EBM-ITM Diagnostic

Steve Cayzer and Peter Mott, Dept of Mechanical Engineering and Vaggelis Giannikas, School of Management, have been one of the early testers and used the Diagnostic Tool on two MSc Courses, Engineering Business Management and Innovation and Technology Management, both of which are centred around team-based learning.

The principle purpose of the diagnostic was to:

  • signpost skills and attributes required on the Courses
  • as a means to link student self-assessment to Practicum training sessions on the Course
  • provide insights in student levels of confidence on core elements of the Courses

ebm-itm Diagnostic responses for Academic Writing
Student responses for Academic Writing

Commenting on the Diagnostic Tool, Steve said: “Adopting this tool to support the team-based learning approach I take on these two courses has enabled me to raise the awareness levels of my students and encourage them to take greater ownership of the skills development they need to meet the demands of their courses. So far it has proved very valuable and I am looking forward to using it further in the coming year.”

Further reading

David Boud (2000) Sustainable Assessment: Rethinking assessment for the learning society, Studies in Continuing Education, 22:2, 151-167, DOI: 10.1080/713695728

Evers, Frederick T., & S. O'Hara. 1996. ‘Educational Outcome Measures of Knowledge, Skills, and Values by Canadian Colleges and Universities.’ Educational Quarterly Review 3 (1): 44.

European Parliament, 2008. Skills and competences are further defined in Appendix D.

Evers, Frederick T., & S. O'Hara. 1996. ‘Educational Outcome Measures of Knowledge, Skills, and Values by Canadian Colleges and Universities.’ Educational Quarterly Review 3 (1): 43–56.

OECD. 2012. AHELO Feasibility Study Report: Volume 1: Design and Implementation: 18. Paris: OECD

Higher Education Funding Council for England (HEFCE). 2014a. Review of the National Student Survey: 28. London: HEFCE

David Nicol (2009) Assessment for learner self‐regulation: enhancing achievement in the first year using learning technologies, Assessment & Evaluation in Higher Education, 34:3, 335-352, DOI: 10.1080/02602930802255139

Themes

  • confidence-based-assessment
  • confidence-gain
  • learning-gain
  • objective setting
  • self-diagnostic
  • self-efficacy

Guidance

Creating a Diagnostic

Diagnostic Demo

Diagnostic CMS

Bath Baseline

UK Professional Skills Framework

Contacts

For technical or pedagogical advice or just simply to talk through ideas or ask questions about using the Diagnostic Tool and CMS contact Kevin Renfrew.