Strategic insights

Strategic context

Drawing on relevant external sources of information to understand the factors affecting the environment in which Monash operates.

Access and Opportunity Report for Monash Commission

Regional and Rural Student Choice Report

Performance-based Funding Evidence Review

An evidence review was performed in order to identify, compare and contrast established approaches to higher education performance-based funding (PBF) models. The aim of PBF is to tie university funding to performance against a range of specified metrics. Specifically, the review aimed to:

Detail the performance metrics included in each of the funding approaches (eg retention measures, graduate outcomes, increase in participation of under-represented groups etc)
Compare and contrast approaches to determine which components are the most common and/or evaluated to be the most effective and
Highlight any unintended consequences identified from PBF approaches.

Results: The review identified 49 different PBF approaches from 25 countries; including information from 22 states within the USA. The differences in model design can be classified into two categories:

1. models which include a standard performance based assessment across the entire jurisdiction or

2. models based on a contract of performance targets between each institution and the governing body.

The number of metrics included in each model ranged from 2 to 18, with evaluations identifying that approaches with >10 metrics were the most challenging to implement and maintain.

The scoping review of models of PBF revealed three main findings. Firstly, the variation in the objectives, design, indicators used and proportion of government funding attached across the 49 models highlights that there is no universally adopted approach. That is, the models are dependent on the government context and local setting and do not subscribe to a 'one size fits all' approach. Secondly, the literature reports a recent shift in the performance targets of PBF models towards a contract based target selection approach which involves an agreed target between each institution and government/funding body as opposed to standard targets applied to all institutions. The contract based target selection approach recognises and supports the diversity of tertiary education providers, differences in institutional missions and may also reflect the growing understanding of the impact of student characteristics (which are beyond the control of the institution) on student outcomes. Thirdly, within the measures selected for inclusion in the PBF models, we observed a wide variation in the measures used, suggesting that there is no consensus on which measures are the most appropriate for each indicator. For example, the number of student completions is reported to be assessed as either: 1) the number of graduates, 2) the completion rate, 3) the share of the total number of graduates in the higher education system or 4) the number of completions within a given study timeframe. In addition, there is currently no evidence examining how the measures were selected to identify whether the selection was based on selection of a given measure or was constrained by data collection methods in place prior to the model design.

Year: 2019. File: Performance-based Funding Evidence Review Report (PDF, Size: 1.2MB)

Factors Facilitating Successful University-Industry Collaborations

Factors Facilitating Successful Research Collaborations between Universities

Cultural Diversity and Cultural Transformation in Higher Education

Recent analysis has revealed that student soft skill development and overall satisfaction are sensitive to changes in the cultural diversity of the classroom. Specifically, the share of international students within the student body and the cultural diversity of this international student population influence soft skill development. The academic literature reports that increased cultural diversity in the university setting leads to improved soft skill development; an effect that is mediated by repeated intercultural interactions. Given the cultural diversity of the enrolled student population at Monash University, there is the potential to capitalise on the structural diversity of the student body and subsequently improve student soft skill development by facilitating repeated and meaningful engagements/interactions between students from culturally diverse backgrounds.

Cultural Diversity Framework

This literature review aimed to identify and examine intercultural interventions and diversity programs within the higher education setting. Specifically the review aimed to identify the features common to programs which had been successful in exposing students to the benefits of diversity to teamwork and critical thinking, and ameliorated tensions arising from cultural difference. The following program features were common to successful programs:

Program facilitates repeated, low level cross cultural interactions,
Programs compulsory and embedded within the curriculum, as opposed to voluntary and ancillary to curriculum,
Group work, peer to peer mentoring and intergroup dialogue improves intercultural interaction/engagement quality and frequency and supports the improvement of student attitutes and beliefs towards intercultural experiences,
Group composition allocated by program leaders to optimise diversity of participants backgrounds and skills, as opposed to student self-selection into groups based on pre-existing friendships,
The benefits of intercultural interactions and expected learning outcomes and benefits explicitly communicated at the outset of the program.

Year: 2019. File: Intercultural Engagement Interventions Report (PDF, 1.4MB)

Program evaluation

Synthesizing data and stakeholder knowledge to maximise the benefit realisation of Monash programs and initiatives.

Understanding the Impact of University Cultural Change on Student Satisfaction

Learning Abroad Benefit Realisation Study

Understanding the Impact of Disadvantage on Co-Curricular Activity Participation

Monash Research Accelerator

Scenario modelling

Using system dynamics modelling to understand the present and plan for the future.

Motivation

Data and analysis can provide information about what has happened in the past but we are also keen to learn about what may happen in the future. While there are no structured data on future performance, there are inputs and scenarios that can be utilised to inform of potential trends that may be faced in the next decade and the responses we may have to these trends. This forecast facilitates investigation into the direct and indirect consequences of the decisions being made about various aspects of Monash's performance.

Monash University as an Eco-System

By considering Monash University as its own eco-system, InSight has developed a system dynamics model that is centred on the student admissions pipeline, staffing, and financial performance. A wide range of other teams within the University have supported the project by providing data and analysis.

SD Model

The model reflects the deterministic nature of university operations such as the student admissions pipeline contributing to the total number of students, and captures feedback loops such as increasing enrolment numbers and the need to recruit additional teaching staff.

Actual data between 2011 and 2017 were used to train and to validate the simulation model. The major forecast outputs include but are not limited to the number of students (domestic v international), staff FTEs by category, and financial positions. Model error rates are kept as low as possible (mostly below 1%) when compared with the training and validating data sets.

The resulting model is a deterministic forecasting model that simulates Monash University’s performance in the defined areas. This tool enables us to understand the impact of different internal and external scenarios on our performance and to investigate the effectiveness of our responses.

How to Interpret the Model

Based on a number of inputs, users can observe changes in the number of commencing students, the number of total enrolments, and the international student share ratio. The simulation can be easily adapted with additional information.

In addition, InSight has collaborated with the finance and risk teams to develop scenarios about what may affect student applications to Monash, yielding a range of possible outputs. These outputs enable us to simulate over one million scenarios, export results, and assign weights as probabilities for each scenario. Resulting predictions should not be viewed as accurate numbers, but rather as trends and ranges that would inform us about potential future scenarios.

Enterprise Intelligence and Insights