CBRThe academic advising archiveCite this work ↗
RMIT University · Master of Engineering · 2003

Enhancing Academic Advisement with Case-Based Reasoning

Tanat Tonguthaisri

School of Electrical and Computer Engineering
Faculty of Engineering · August 2003

Can experience from past student cases help an advisor make the next course recommendation?

Original research, preserved. Interactive explanations, added in 2026.

Reasoning through experience Interactive guide
A library
of prior cases

RetrieveFind prior cases whose circumstances resemble the new problem.

Select a stage. Learning guide based on the thesis, pp. 40–43.

2003Thesis date · August
201Pages in the supplied repository PDF
5 chaptersPlus bibliography and appendices A–E
2026Repository deposit · digital reconstruction
The original research

Abstract

Author’s abstract · 2003
Source: printed p. 1 ↗

This research involved the study of an application of Case-Based Reasoning (CBR) in the field of academic advising. Academic advisement has been recognised as an important function of higher education. However, there is a widespread desire to improve the advisement services provided by many educational institutions (e.g., colleges and universities) around the world. In the last ten years, CBR expert systems have made important contributions in many domains by applying CBR methodology to a variety of areas of human problem solving. However, to date, expert advisement systems have not been successfully applied to academic advisement, which seems to be a very appropriate domain for this technology.

Academic advising is an important and time consuming task, and different tools and techniques must be used to make it an effective and efficient process. This thesis describes the design, development, and validation of a prototype case-based academic advisement recommender system that supplements the conventional advising process, and could eventually evolve to replace human academic advisors when its full potential is realised.

Historical wording is preserved. Statements about the state of the field and possible future capabilities describe the thesis’s 2003 context.
01 / The argument

Use experience for advice.
Use rules for constraints.

The thesis separates two jobs: choosing a useful elective and deciding whether a student meets a course’s requirements. Its prototype brings these approaches together.

01

Find a relevant precedent

Represent a student’s situation as a case. Retrieve cases with similar interests, academic performance and specialisation preferences to guide elective selection.

Conclusion · printed pp. 140–141 ↗
02

Apply explicit constraints

Use rule-based reasoning for core courses and prerequisite checks. A close match does not make an unavailable course an appropriate recommendation.

Conclusion · printed p. 140 ↗
03

Keep the advisor in the loop

The thesis describes a prototype and calls for advisor cross-checking. Exceptional situations and key decisions still need human judgement.

Limitations · printed p. 142 ↗
Scope of the evidence. The thesis presents prototype development and test-case validation. This companion does not claim a production deployment, measured student outcomes, or validation against today’s systems.
02 / Try the idea

Similar does not always
mean eligible.

Change a fictional student profile. See how a similarity ranking changes, then how a prerequisite rule changes what can be recommended.

A small advising sandbox

Six invented cases. One transparent similarity formula. No student data is collected.

Demo similarity = interest weight × exact interest match + remaining weight × performance proximity. Percentages are similarity scores, not probabilities or reported thesis results.

Most similar invented casesSimilarity

03 / Follow the evidence

The complete thesis,
with a way in.

Read the source in sequence, jump to a chapter, or search across the proofread text. Every page links back to the original scan.

Read the 2026 reading and discovery guide ↗ · Explore the image QA lab ↗

Looking for a specific idea?

Search similarity, prerequisite, validation, case indexing, or any term in the thesis.

Search proofread text ↗

Source note: the scan’s printed numbering jumps from 134 to 136. The archive preserves all 201 supplied PDF pages; the reason for the missing printed number is unresolved.

04 / Read, cite, build on

A stable reference.
An accessible archive.

Cite the 2003 thesis with its RMIT repository DOI. Use the original scan to check exact quotations, equations, tables and code.

Tonguthaisri, T. (2003). Enhancing Academic Advisement with Case-Based Reasoning [Master of Engineering thesis, RMIT University]. https://doi.org/10.25439/rmt.34846194

BibTeXRISCSL JSON

Thesis year: 2003. Repository deposit: 30 October 2026. The DOI identifies the RMIT record, not a new thesis or a claim that RMIT endorses this companion.

A note on this edition

Preservation with
clear boundaries.

Version 2 was checked against all 201 supplied PDF pages. Transcription corrections are recorded separately from errors and unreadable regions in the original source. The original PDF and raw OCR remain available for comparison.

The diagram supplied by the author in 2026 reconstructs the redacted CBR cycle and includes a Watson extension. It is labelled as an editorial reconstruction, not as a recovered facsimile of the original illustration.

The explanations and sandbox on this page are new teaching material. They do not change the thesis’s text, date, authorship or findings. Read the edition and source notes ↗