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Learning Analytics Procedure

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Section 1 - Purpose

(1) This Procedure establishes how the University of Canberra (the University) and University of Canberra College (UCC) use learning analytics to monitor student engagement and success within units and to identify students at risk of not successfully completing the unit.

(2) This Procedure focuses on the purposeful creation of learning analytics to proactively improve education and learning experiences for students.

(3) This Procedure provides guidance on how learning analytics facilitate the integration of data from multiple sources, including:

  1. student enrolment load, past academic results, grade point average (GPA) and demographics (via student management systems);
  2. engagement and performance within enrolled units during a teaching period (via learning management system);
  3. access logs relating to learning resources and activities (via learning management  system); and
  4. student feedback on educational experiences to inform institutional monitoring, review and improvement.

(4) The University provides student learning, engagement and employability services including peer-assisted learning, study skills, academic skills and discipline specific skills to assist in the successful completion of their units of study.  Refer to the Support for Students Policy.

(5) This Procedure should be read in conjunction with the University of Canberra (Academic Progress) Rules 2022, the Support for Students Policy and the Academic Progress (Coursework Units) Procedure to ensure that the use of learning analytics supports students through early identification of a need for intervention by academic, non-academic or personal (for example, wellbeing) support services.

(6) This Procedure supports the University in achieving compliance with Standards 1.1, 1.3, 1.4, 2.2.3, 3.1, 3.3, 5.3, 5.4 and 6.3 of the Higher Education Standards Framework (Threshold Standards) 2021.

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Section 2 - Scope

(7) The Procedure applies to:

  1. students enrolled in coursework courses at the University and UC College;
  2. students enrolled in Higher Degree by Research (HDR) courses at the University;
  3. students enrolled in coursework units as part of non-award studies; and
  4. academic, professional and executive staff who oversee and support student learning and progression.
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Section 3 - Policy

(8) This Procedure supports the Support for Students Policy. 

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Section 4 - Procedure

Data purpose and collection

(9) Learning analytics are used by the University to support decisions relating to student support and student progression, including over-enrolment, late enrolment, late withdrawal and conditions of academic probation.

(10) The University collects information about students’ academic performance and engagement in accordance with the University’s Privacy Policy, with the aim of improving student success and retention.

(11) Student data is collected and presented as learning analytics to facilitate the identification of issues and to provide timely interventions to support student success. Actionable analytics enable academic and professional staff to respond to educational needs, including tailoring the design and delivery of the student experience as required.

(12) Learning analytics tools collate relevant data about students’ engagement and participation in a unit, and based on numerous variables students may be assigned a risk category, such as low, medium or high risk of not meeting the learning outcomes of a unit.

(13) The student data collected under Clause 10 is drawn from the University’s student management systems and learning management system and is visualised through digital dashboards designed to promote learner and teacher engagement in educational partnerships.

(14) A UCLearn analytics dashboard within each unit displays metrics such as grades (individual and average), submission indicators including percentage on time submissions, and number of missed assignments, engagement/activity indicators such as count of page views and count of contributions. Based on these metrics, students at-risk of not meeting the learning outcomes of a unit can be further categorised as follows:

  1. students who are trying and/or are motivated and at risk of not passing; and
  2. students who are not trying and/or are not motivated and at risk of not passing.

(15) Learning analytics support the identification of students ‘at-risk’ of not meeting the learning outcomes of a unit through two measures which are weighted accordingly:

  1. Engagement measures calculated based on the students’ use of learning resources, participation in learning activities and interaction with other elements within unit’s learning environment, including the University’s online learning environments.
  2. Progress measures calculated based on actual completed credit points, and submission of assessment tasks and predictions of academic risk (which incorporate engagement).

(16) The use of learning analytics to identify students ‘at-risk’ of not meeting the learning outcomes of a unit should be considered within the context of the unit offering. Participation in online learning activities may not have any relevance in a unit with corresponding on-campus learning activities which a student may participate in, while it would have significance in a unit which is delivered through scheduled real-time learning activities.

(17) Information on student risk indicators from learning analytics is displayed in the unit convener and program/discipline level dashboards from the commencement of the teaching period to allow suitable support to be offered to  students.

(18) Staff who become aware of students at-risk of unsatisfactory progress at any time under Clause 16 should intervene. Refer to the Academic Progress Policy.

(19) A Unit Convener uses learning analytics to undertake unit level monitoring and review during a teaching period for:

  1. a coursework unit in accordance with the Academic Progress (Coursework Units) Procedure, and
  2. an honours (H) unit in accordance with the Bachelor Honours Degree Thesis Procedure.

(20) Learning analytics can be used for monitoring and review purposes within HDR courses in accordance with Higher Degree by Research (HDR) Procedure.

(21) Staff should continue to encourage students who are identified as low risk of unsatisfactory academic progress and congratulate cohorts on their achievements to promote a healthy learning environment.

Data access and communication

(22) Collated learning analytics data is accessible to the faculty Dean, Associate Dean, Education (ADE), Head of School/Discipline Lead (or equivalent), Program Directors/Course Conveners (or equivalent), and Unit Conveners at the specific levels for which they are responsible.

(23) The University responds to engagement, progress and risk data gained through learning analytics tools in a timely and appropriate manner by providing:

  1. students with an overview of their engagement and progress in the units they are enrolled in, as well as their overall progress towards course completion (including total credit points, grade point average (GPA) and weighted average mark (WAM)) via MyUC;
  2. staff with information on how students in their units are progressing in terms of relevant engagement and performance measures; and
  3. the University’s executive with comparative data on retention, engagement, progress and risk, as well as the overall progress and achievement of learners across the University.

Data monitoring

(24) Students can self-track and monitor their own engagement, achievement and progress in their units by using data and prompts within the University’s online learning environment to:

  1. increase their engagement in their unit(s);
  2. monitor timely submission of assessment tasks; and
  3. understand their overall course progress, based on confirmed results of previously enrolled units, and performance in currently enrolled units.

(25) Faculty Deans, ADE (or equivalent), and Heads of School/Discipline Leads (or equivalent must ensure that staff regularly review the learning analytics provided in their digital dashboards, and use the data to:

  1. identify and address student academic needs, including any issues of significant concern or additional support required by students;
  2. increase student engagement;
  3. inform enrolment criteria;
  4. improve the student experience through academic and non-academic support;
  5. assure the quality of unit and course-level outcomes;
  6. assist with course and unit design and monitoring, review and improvement; and
  7. review feedback on their teaching and research supervision.

(26) An annual report is submitted to the Academic Quality and Standards Committee (AQSC) by the Deputy Vice-Chancellor that may include:

  1. details on student numbers according to their academic standing and whether an Academic Improvement Plan is in place;
  2. student attendance rates at student support services;
  3. identification measures of students at-risk and their association with resulting unit outcomes;
  4. engagement measures; and
  5. unit success and associated retention/attrition rates based on broad demographics used in learning analytics.

Data privacy

(27) The University handles all student data in accordance with its Privacy Policy.

(28) Overall analysis of students’ satisfaction comments, extracted via the digital dashboards, will not include student names, numbers or other identifying information.

(29) Student comments submitted in the digital dashboard will be displayed anonymously in the unit convener dashboard.

(30) Staff can flag comments from the digital dashboard which raise concerns for student safety or may be considered a breach of the University of Canberra (Student Conduct) Rules 2023. Such comments will be able to be identified if required by selected senior staff within central business units to allow the student to be contacted as necessary.  

(31) Flagged comments will be initially triaged by Student Wellbeing & International Support (SWIS) to address any immediate welfare concerns. Comments which are flagged for reasons other than welfare concerns will be sent to the Associate Director, Learning & Teaching for review and appropriate action. Staff may request removal of concerning comments via a Service Desk request.

(32) An executive-level dashboard will display student identifiers to allow selected senior staff to contact students, when necessary, to improve their educational experiences and provide individualised support in instances where there is a welfare concern.

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Section 5 - Roles and Responsibilities

WHO
RESPONSIBILITIES
Academic Quality and Standards Committee (AQSC)
Associate Dean, Education (ADE)
  • Monitoring executive-level aggregated learning analytics, including summary data of student risk categories, and acting as required.
Deputy Vice-Chancellor (DVC)
  • Notifying staff of the University’s obligations under the Education Services for Overseas Students Act 2000.
  • Ensuring that systems can accurately capture data on student results to allow for input into the learner analytic software.
  • Submitting an annual report to the Academic Quality and Standards Committee (AQSC).
  • Monitoring learning analytics to generate university-wide improvements that strengthen teaching and partnerships.
  • Offering a comprehensive range of academic and non-academic support options for students to assist in the successful completion of their units of study.
Director, Student Connect
  • Monitoring the academic progress of a student at the course level.
Director, Student Life
  • Provision of non-academic support options for students to assist in the successful completion of units of study.
Digital, Information and Technology Management (DITM)
  • Supporting the integration of student feedback solutions with the virtual learning environment and administration systems.
  • Undertaking testing of integrations and new components of the dashboards and enhancements.
  • Reporting to the Deputy Vice-Chancellor (DVC) on status of student feedback system developments.
  • Responding to stakeholder requests for fixes/enhancements of the student feedback system.
Learning & Teaching (L&T)
  • Supporting the integration of unit level student feedback solutions with the virtual learning environment and administration systems.
  • Undertaking testing of integrations and new dashboards components and enhancements.
  • Reporting to the Associate Director, Learning & Teaching on the status of system developments related to unit level student feedback solutions.
  • Responding to stakeholder requests for fixes/enhancements and gathering requirements for unit level student feedback solutions.
  • Overseeing the incorporation and ongoing improvement of learning analytics systems.
  • Monitoring learning analytics to:
    • ensure system-level feedback is identified and responded to appropriately.
    • inform the Deputy Vice-Chancellor (DVC) of features that indicate strengths and weaknesses in the University’s learning environment.
  • Providing staff training and development opportunities in monitoring and responding to learning analytics.
  • Assisting staff in employing learning analytics to inform recognition and advancement strategies associated with teaching excellence.
  • Providing access to learning resources and scholarly information related to learning support, to University staff (including third-party provider staff where relevant).
  • Producing annual reports (from learner analytic summaries) as defined in these procedures (or by teaching period if required) to give an overview of the summarised data and report on key trends or areas of concern, as outlined in this document.
  • Providing ongoing staff development and training to academic staff in relation to learner support and intervention strategies.
  • Ensuring that staff receiving analytic reports are aware of their obligations to refer students to the relevant support services and can interpret data and reports.
Line Managers of teaching staff
  • Employing learning analytics and reviewing teaching staff responses to learning analytics to inform professional discussions and performance reviews in accordance with the Performance Expectations Policy.
Program Directors/ Course Conveners (or equivalent)
  • Providing course level advice and ensuring appropriate communications within course materials/background information to inform students of the support services available at the University.
  • Monitoring course level aggregated learning analytics including summary data of student risk categories, and act as required.
  • Monitoring learning analytics for all units within the course/program to:
    • gain an overall understanding of the student experience;
    • quality assure (benchmark and moderate) unit and course-level outcomes; and
    • compile reports using comparative internal and external datasets (for example, QILT).
  • Ensuring teaching staff within a course/program respond effectively to learning analytics information.
  • Employing learning analytics to assist with course design and coherence.
Students
  • Seeking assistance and providing relevant information to the University to allow their needs to be supported.
  • Checking and responding to University email regularly and being aware of administrative responsibilities (as detailed in the Student Charter, Enrolment Policy and Enrolment Procedure, unit outlines and other course materials).
  • Participating actively and positively in the teaching-learning process and complying with the requirements of their course or unit of study.
  • Monitoring learning analytics to self-track academic achievement and course progress and responding to learning analytics by adapting engagement activities and adjusting study habits.
  • Providing teaching staff with constructive and actionable feedback on their student experience.
Unit Convener
  • Targeting support for those who need it and referring students to the relevant support services in a timely way.
  • Engaging with Learning & Teaching (L&T) training and information on the interpretation of learner analytics reports and active support of students’ learning.
  • Monitoring learning analytics to support students’ academic achievement and course progress.
  • Responding to learning analytics to support students in adapting engagement activities and adjusting study habits.
  • Implementing intervention strategies that address possible risk factors identified via learning analytics and promote student success.
  • Activating a dynamic learning environment that is responsive to student feedback, evidence-based and reflective.
  • Employing learning analytics to prompt and evaluate continuous improvements in the quality of teaching and to inform professional development.
  • Undertaking unit level monitoring and review during a teaching period for:
University of Canberra College
  • Identifying students at risk of not making satisfactory progress.
  • Working in partnership with UC faculties to develop strategies that enhance student support
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Section 6 - Definitions

TERM
DEFINITION
Academic failure (at risk of)
Academic failure includes the following:
  • actions by the student that increase the likelihood of failing one or more units, including low rates of participation whether face-to-face or online;
  • late or non-submission of assessment tasks;
  • achieving a mark of less than 50 in an assessment task;
  • achieving a final mark that is less than 50; and
  • failing to meet a hurdle requirement (where relevant).
Australian Qualifications Framework (AQF)
The national policy for regulated academic qualifications in Australian education and training. It incorporates the quality assured academic qualifications from each education and training sector into a single comprehensive national academic qualifications framework.
At-risk student
A student with a personalised data-profile which includes evidence that the student is at risk of academic failure.
Data
Any quantitative or qualitative data either provided to the University by students or derived from internal data sources, this may include data derived and analysed from multiple sources.
Digital dashboard
Enables Unit Conveners to:
  • engage with their students and respond to feedback provided by them;
  • review students’ engagement with learning materials, activities and assessments; and
  • identify ‘at-risk’ students.
Provides executives with data including total student numbers defined by risk category, satisfaction and demographics, by faculty, discipline, course and unit.
Engagement Measures
Engagement measures are calculated based on the students’ use of learning resources, participation in learning activities and interaction with other elements within the unit’s learning environment, including the University’s online learning environments.
Learning analytics
The measurement, collection, analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which it occurs.
Learning management system
The online location where assessment is submitted by students with feedback and progressively awarded Marks and Grades entered by staff for each Coursework Unit (as defined in the Assessment Policy).