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The purpose of this paper is to examine issues academics at importing institutions face while delivering Australian degrees in Malaysia. Transnational higher education (TNE) has been widely researched. However, less widely researched is the area of understanding what academics at the offshore locations need to uphold the required academic standards of their partnered exporting universities. This area warrants close attention if Australian and other transnational education universities are to sustain their growth through a partnership model with offshore academics delivering a portion (often a substantial portion) of the teaching.

Two focus groups were conducted with a mix of long standing and newly recruited Malaysian lecturers who taught into an Australian degree through a partnership arrangement. The semi-structured questions which were used were derived from a preliminary literature review and previous internal institutional reports.

The findings from the focus groups indicate that TNE is largely “Australian-centric” when addressing the standard of academic quality and integrity. The findings pointed not so much to any sustained internationalisation of curriculum or administration or personnel but more as internationalisation as deemed required by the local academic.

To a greater extent, the findings highlighted that equivalent student outcomes do not necessarily equate to equivalent learning experiences or teaching workload. In fact, the frustration of the interviewees on the tension to fulfil the home institution curriculum and helping students to “comprehend” an Australian-centric curriculum translates to “additional and unrecognised workload” for the interviewees.

The effort in addition to relevance is a major factor for satisfaction and utility of the document to the actual user. The purpose of this paper is to propose a method in generating relevance judgments that incorporate effort without human judges’ involvement. Then the study determines the variation in system rankings due to low effort relevance judgment in evaluating retrieval systems at different depth of evaluation.

Effort-based relevance judgments are generated using a proposed boxplot approach for simple document features, HTML features and readability features. The boxplot approach is a simple yet repeatable approach in classifying documents’ effort while ensuring outlier scores do not skew the grading of the entire set of documents.

The retrieval systems evaluation using low effort relevance judgments has a stronger influence on shallow depth of evaluation compared to deeper depth. It is proved that difference in the system rankings is due to low effort documents and not the number of relevant documents.

Hence, it is crucial to evaluate retrieval systems at shallow depth using low effort relevance judgments.

The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback.

CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, focussing on 20 queries.

Both Mean Average Precision and Normalized Discounted Cumulative Gain results indicate CoRRe to have the highest retrieval precisions at all the three levels compared to the other feedback models. Furthermore, independent t-tests showed the precision differences to be significant. Rating was found to be the most popular technique among the participants, producing the best precision compared to referral and comments.

The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users.

The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience.

In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues.

This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods.

The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments.

Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.