Peer mentoring is often considered the single most effective strategy for increasing student retention and student satisfaction. As a consequence, mentoring programs have been implemented at most universities and are an essential feature of best practice transition programs. Yet, the literature is inconsistent regarding what the term entails and how it is applied, leading to diverse opinions about what constitutes a mentoring program. It could be argued that agreement on a definition of mentoring is secondary to the benefits of its practice and that an emphasis on terminology is just playing semantics. However, this article argues that terminology does matter and that elucidating what mentoring entails is crucial to the comparative evaluation and improvement of mentoring practice as well as the identification of best practice. The article goes on to suggest how mentoring boundaries might be set by drawing on experiences from an Australian University.

Effective teaching and learning in higher education is an important focal point of literature around the globe. Various models are presented as desirable and fostering optimal conditions for teaching and learning. However, each model must be examined within the context of its institutional culture, mission and strategic plan to ascertain if it meets the envisaged goals. The Reinventing Teaching Project survey conducted at Flinders University in 2009 provided a unique opportunity for academic staff and students across all faculties to respond to a survey that explored their perceptions of optimal learning conditions and assessed if the campus environment was conducive to effective teaching and learning practices. The exploratory study was designed to gather qualitative and quantitative data on the motivation of teachers and learners to engage with learning and learners (or not). The results of the survey present valuable insights into what teachers and learners consider to be important attributes of optimal teaching and learning and indicate a number of similarities and differences among teacher-student perceptions. This paper identifies and discusses some of the pertinent outcomes of the study to provide a framework for other similar studies. IntroductionWith its blend of on-campus, cross-institutional, outreach and off-shore teaching cultures, Flinders University offers valuable opportunities and resources for constructing narrative frameworks about contemporary tertiary teaching and learning effectiveness. To investigate the teaching and learning perceptions of its community, Flinders University carried out two significant studies over the last decade: the first in 1996-1997, which surveyed the views of academic staff only, and another, undertaken in 2007-2009, which investigated the perceptions of both staff and students.In 1996 Flinders University embarked on a campus-wide study to capture data about the teaching practices of its academic staff and their views on tertiary teaching. Among a broad range of issues highlighted in the study conducted and published by Cooper, Lawson and Orrell in 1997, it was found that good planning, adequate resources and collaborative practices were desirable conditions for strengthening positive learning environments. The project utilised a questionnaire (242 surveys

This article reports on the development and implementation of the Student Success Program, a strategic, University-wide, consistent approach to increasing the success and retention of first year students, during 2014. To this end, a centrally coordinated process integrated historic student, application, enrolment, academic performance and learning management data in a data warehouse. These data were used to build chi-squared automatic iterative detection (CHAID) decision tree models aimed at predicting each student's risk of attrition. Predictions were made multiple times per year before peak attrition time points to allow for changes in student behaviour and availability of new data. An intervention using peer-to-peer phone-call communication targeted students with the largest predicted risks, to offer support, foster retention and enable a successful outcome in higher education. The accuracy of the chi-squared automatic iterative detection models benefited most from the inclusion of data representing first year student study behaviours.

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