HE-C05 — Student Success and Support

Course Code: HE-C05  |  School: School of Higher Education  |  Cluster: Curriculum and Teaching

Level: Intermediate  |  Estimated duration: 6 weeks (approximately 40 hours)  |  Language: English  |  Certificate: Professional Certificate (non-degree)  |  Format: Self-paced with AI support under human supervision

Overview

Student success is the most heavily funded and least well understood commitment in contemporary higher education. Institutions invest in orientation weeks, peer mentoring, learning centres, wellbeing services and early-alert dashboards, yet completion gaps between advantaged and disadvantaged students have proved remarkably stubborn across four decades of intervention. This course treats that gap as an empirical puzzle rather than a motivational one, and asks what the evidence actually supports.

The course begins with the theoretical tradition that has dominated the field since the 1970s: Vincent Tinto’s interactionalist model of departure, and the sustained critique of that model from scholars who argue it pathologises students rather than institutions. Learners will read the critique alongside the original, and will be asked to decide which parts of the framework survive contact with commuter students, mature learners, first-generation entrants and part-time students in systems very different from the residential American college for which it was designed.

The second half is practical and evaluative. Learners design a support intervention for a real or realistically specified institution, define what success would look like in measurable terms, and construct an evaluation plan capable of detecting whether the intervention worked. Considerable attention is given to the difference between an intervention that improves outcomes and one that merely selects students who would have succeeded anyway.

Learning outcomes

On completion, a successful learner will be able to:

1. Explain the principal theoretical models of student retention and departure, and identify the assumptions each model makes about students and institutions.
2. Critically evaluate the empirical evidence for widely adopted success interventions, distinguishing well-supported claims from claims that rest on weak or selective designs.
3. Analyse institutional data to identify where and when attrition occurs and which student groups are disproportionately affected.
4. Design a student support intervention that is specified precisely enough to be implemented and evaluated by someone other than its author.
5. Construct an evaluation plan with defined outcome measures, a credible comparison strategy and an account of plausible confounds.
6. Assess the ethical implications of predictive analytics and early-alert systems, including the risk of labelling and self-fulfilling prophecy.
7. Communicate findings and recommendations to institutional decision-makers in a form that survives budgetary scrutiny.

Who this course is for

Student affairs professionals, academic advisers, widening participation and access officers, programme leaders, institutional researchers, and academics who carry responsibility for retention or progression outcomes. It is equally suitable for policy staff in ministries and funding bodies who set completion targets.

Prerequisites

No formal prerequisites. Learners should be comfortable reading tables of institutional data and interpreting percentages and rates. Prior completion of HE-F01 Foundations of Higher Education is helpful but not required.

Syllabus

Module 1 — What We Mean by Student Success

Success is defined differently by regulators, institutions, employers and students themselves, and the definition chosen determines which interventions appear to work. This module makes those competing definitions explicit before any measurement begins.

Lessons. 1.1 Completion, progression, attainment and employment as rival metrics  ·  1.2 Equity gaps and how they are calculated  ·  1.3 The student voice and definitions success measures omit  ·  1.4 Constructing a defensible success framework for one institution

Deliverable. A two-page diagnostic note stating the success definition your institution uses, the definition it should use, and the consequences of the difference.

Module 2 — Theories of Retention and Departure

Tinto’s integration model, Bean and Metzner’s attrition model for non-traditional students, and Bourdieu-informed accounts of cultural capital give incompatible explanations for the same outcomes. The module treats the disagreement as productive rather than as a problem to be resolved.

Lessons. 2.1 Tinto’s interactionalist model and its residential assumptions  ·  2.2 Critiques from scholars of race, class and commuter experience  ·  2.3 Belonging, stereotype threat and the social-psychological turn  ·  2.4 Applying competing models to a single institutional case

Deliverable. An analytical brief applying two competing retention theories to the same attrition pattern and explaining what each theory makes visible and invisible.

Module 3 — Institutional Data and the Anatomy of Attrition

Attrition is rarely uniform: it clusters in particular modules, particular terms and particular student groups. This module develops the analytical habits needed to locate it precisely before designing any response.

Lessons. 3.1 Cohort tracking, survival analysis and the limits of aggregate rates  ·  3.2 Identifying bottleneck modules and critical transition points  ·  3.3 Disaggregation by entry route, background and mode of study  ·  3.4 Data quality problems and how they distort conclusions

Deliverable. A design artefact: an annotated attrition map for one programme or faculty identifying the three highest-leverage intervention points.

Module 4 — Evaluating What Works

Most published claims about support interventions rest on comparisons between students who chose to participate and students who did not, a design that cannot separate the effect of the intervention from the characteristics of the participants. This module teaches learners to detect that flaw and to design around it.

Lessons. 4.1 Selection bias and the volunteer problem in support evaluation  ·  4.2 Randomised, quasi-experimental and matched comparison designs  ·  4.3 Reading the What Works and belonging-intervention literature critically  ·  4.4 Effect sizes, replication failures and what generalises across contexts

Deliverable. An evaluation report assessing the evidentiary strength of three published claims about a student success intervention.

Module 5 — Designing and Implementing Support

Well-designed interventions fail routinely at the implementation stage because of staffing, referral pathways, timing or student non-take-up. The module treats implementation as a design problem in its own right.

Lessons. 5.1 Proactive advising, peer mentoring and structured first-year experience  ·  5.2 Curricular versus co-curricular placement of support  ·  5.3 Early-alert systems, predictive analytics and their ethical limits  ·  5.4 Take-up, stigma and the design of non-stigmatising provision

Deliverable. An implementation plan with resourcing, staffing, referral pathways, a timeline and a stated failure mode.

Module 6 — Capstone: A Student Success Case

The capstone integrates definition, theory, data, evaluation design and implementation into a single institutional proposal. It is assessed as a document that could plausibly be submitted to a senior committee.

Lessons. 6.1 Framing the problem with evidence  ·  6.2 Selecting and justifying the intervention  ·  6.3 Specifying the evaluation  ·  6.4 Writing for institutional decision-makers

Deliverable. A 2,500-word student success case for one named institution, with an attrition analysis, a specified intervention, an evaluation plan and a costed implementation schedule.

Assessment

Pass mark 70 per cent. All assessed components must be attempted. Every mark in this course is issued by a human assessor; no assessment outcome is generated automatically.

Rubric criteria

Each assessed artefact is marked against four criteria at four levels (distinction, pass with merit, pass, fail).

1. Evidential rigour. Claims are supported by data or literature, and the strength of the support is characterised accurately.
2. Theoretical command. Retention theories are used analytically rather than cited decoratively, and their limits are acknowledged.
3. Design feasibility. The proposed intervention and evaluation could be executed with the resources and constraints stated.
4. Ethical and equity awareness. Effects on disadvantaged groups, and the risks of labelling and surveillance, are addressed explicitly.

Reading list

Core. Tinto, V. (1993) Leaving College: Rethinking the Causes and Cures of Student Attrition, 2nd edn. Chicago: University of Chicago Press.

• Tinto, V. (2017) ‘Through the Eyes of Students’, Journal of College Student Retention: Research, Theory & Practice, 19(3), pp. 254–269.
• Yorke, M. and Longden, B. (2004) Retention and Student Success in Higher Education. Maidenhead: Open University Press.
• Thomas, L. (2012) Building Student Engagement and Belonging in Higher Education at a Time of Change. London: Paul Hamlyn Foundation, What Works? Student Retention and Success Programme.
• Kuh, G. D., Kinzie, J., Schuh, J. H. and Whitt, E. J. (2010) Student Success in College: Creating Conditions That Matter. San Francisco: Jossey-Bass.
• Walton, G. M. and Cohen, G. L. (2011) ‘A Brief Social-Belonging Intervention Improves Academic and Health Outcomes of Minority Students’, Science, 331(6023), pp. 1447–1451.

Academic integrity and use of AI

Generative AI may be used in this course as a drafting, translation and critique aid, and its use must be disclosed. Every submission carries a short use-of-AI statement naming the tools used, the tasks they performed and the checks the learner applied to the output. Using AI to fabricate data, invent sources, impersonate an interview participant, or produce an artefact the learner cannot explain in a live viva is prohibited.

All citations are verified before submission; a reference that cannot be located by the assessor is treated as fabricated. Fabrication, plagiarism and undisclosed ghost-authorship result in failure of the component and referral to the academic integrity panel. Marks in this course are issued by human assessors, and any learner may be asked to defend a submission orally before a mark is confirmed.