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HOW DATA FROM DIGITAL LEARNING TOOLS CAN REFINE TEACHING

Digital learning tools enable educators to quickly collect and analyse student
performance data in order to refine their teaching, as Paul Moss explains

Teaching and learning
Course design and delivery
Assessment and quality assurance
Oceania
Feature article

PAUL MOSS

The University of Adelaide

Created in partnership with

The University of Adelaide


26 Sep 2022
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Used effectively, digital tools can help educators collect and analyse student
performance data to help them refine their teaching practices. One example is
the Learning Mastery Gradebook view in the Canvas learning management system
(LMS), which helps collect data on progress made towards achieving individual
course learning outcomes and presents the information a highly visual way.

The average student attainment for each learning outcome is displayed in real
time at the top, providing a clear snapshot of the overall cohort’s success or
failure in each outcome. The progress of individual students can also be seen.

To populate the Learning Mastery Gradebook view, outcomes are added to
assessment rubrics and marked in the Canvas grading tool, SpeedGrader, where you
can view and grade student assignment submissions in one place using a simple
point scale or rubric. Pedagogically, the strength of using rubrics in this way
is in the explicit promotion of constructive alignment, in which teaching and
assessment are aligned with desired learning outcomes.

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CONSTRUCTIVE ALIGNMENT

Course co-ordinators are encouraged to thoughtfully map course learning outcomes
to assessment and then design rubrics that serve three purposes:

 * to guide students towards achieving success in the task
 * to help those marking their work maintain consistency
 * to act as a checklist to evaluate if the criteria being assessed sufficiently
   match the content taught.

This is where the design process can be powerful: if there is an obvious
disconnect in alignment among outcomes, assessment and content, then adjustments
can be made.

If outcomes are measured in more than one assessment, Canvas has a feature where
the outcome scores can be aggregated. The designer can choose the weighting of
an outcome each time it is measured, meaning they can give a particular
assessment a stronger bearing on the overall attainment measure.

Compared with overall course grades at the end of a semester, individual
learning outcome data provide a more detailed understanding of how each outcome
affects or contributes to the whole. Using this approach, the educator can
reflect on why certain outcomes were or were not successfully attained, the
impact that any weakness may have had on overall performance, and what could be
done to improve outcomes in future versions of the course.


MORE PRECISE FEEDBACK: A CASE STUDY

Ivan Obaydin, senior lecturer in the University of Adelaide Business School, has
taken the use of outcomes in Learning Mastery Gradebook to a more granular
level. Rather than looking at course learning outcomes aggregated as a single
figure based on all relevant assessments, Ivan breaks them up, collecting data
on outcomes tied to each individual assessment.

For example, the mid-semester exam tests learning outcomes one and six, labelled
MSE-LO1 and MSE-LO6 respectively. The same learning outcomes are tested again in
the final exam, labelled FE-CLO1 and FE-CLO6. Visually, he can quickly see when
students have struggled with a certain learning outcome and when they have
improved or perhaps regressed.

This data can then be aggregated, so Ivan can see how students fared in each of
the outcomes over the semester and identify where refinement might be needed for
the next roll-out of the course.

Taking an example from Ivan’s course, he noticed that students had performed
worse in two learning outcomes compared with the others, so he investigated why.
He was particularly interested in why performance dropped so much between the
mid-semester and final exams.

As a result of his evaluation, Ivan made adjustments to the delivery and
teaching of the course for this semester. He placed greater emphasis on
explanations of specific content in his lectures and began using the LMS to
support more active learning and exercises to deepen students’ understanding of
concepts.

Ivan encouraged tutors to focus more on these two outcomes in tutorial
activities enabling students to practise their application. Ivan believes the
changes have already produced positive results, with students showing increased
engagement and willingness to answer questions.

Herein lies the power of this approach. This form of action research is a
proactive way of using data to inform design. The changes Ivan made to this
semester’s course will shape the next round of student performance data, and
this in turn will allow him to analyse if the proposed adjustments have made a
difference.


TAILORING LEARNING TO THE NEEDS OF THE COHORT

As course coordinator, Ivan can see the potential of this data-informed approach
to support tutors. The Learning Mastery Gradebook data can be manipulated so
they highlight individual “sections” in the gradebook. These sections usually
represent tutorial groups. This means he can see if the learning outcome
weakness is across all students or just particular groups.

Isolating the data enables Ivan to open conversations with tutors about how
their students progressed in each outcome. Such data could inform a tutor that
more time should be spent on certain outcomes in the lead-up to the final exam.
Deeper analysis could provide evidence of certain patterns (for instance,
outcomes that rely heavily on previous knowledge), so future cohorts could be
grouped on this basis. Or tutors may increase their emphasis on prior knowledge
in retrieval activities, for example.

Using data in this way, university teachers can continually tailor curricula and
course delivery to suit the needs of their cohort.

Paul Moss is a learning design and capability manager at the University of
Adelaide.

If you found this interesting and want advice and insight from academics and
university staff delivered direct to your inbox each week, sign up for the
THE Campus newsletter.





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Secure and transparent use of…
Balancing student data collection and…
Monitoring student engagement via…
Diversity statements: what to avoid…
Emotions and learning: what role do …
Decolonising the curriculum – how do I get started?
Authentic leadership: the four pillars of…
Virtual reality has failed education, so…
Secure and transparent use of student data
Balancing student data collection and privacy protection
Monitoring student engagement via online teaching tools
Bronwen Swinnerton and James Pickering outline the steps all universities should
take to ensure ethical and transparent collection and use of student data via
ed-tech platforms
Increased scrutiny of universities as keepers of valuable data means
institutions need to be well versed in data protection responsibilities. Andy
Phippen offers key aspects to consider
Maurice Kinsella and colleagues offer practical advice on using the virtual
learning environment (VLE) tools to monitor student engagement and focus support
efforts where they are needed
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