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March 2017


Professor Peter Reimann

The widespread availability of learner-related data has the potential to empower students, teachers, parents and school leaders by providing critical insights into the learning process. However, fostering a culture of data-informed learning and teaching in schools remains a significant challenge. This is in part due to capacity: Teachers are by and large not prepared for advanced data practices, and teacher education providers are currently not well prepared to develop that capacity. Research on developing data literacy for teachers, research so far has focussed mainly on three aspects: 1) analysis of the components that make up this literacy, 2) analysis of existing teacher education programs, and 3) studies on professional development programs conducted with in-service teachers. After an overview of the state of the art, I will address questions regarding What to learn and How to learn about data literacy in more detail, focusing on pre-service teacher education because very little is known at this stage about how to design for and support the learning of education students.

Event details
When: Wed April 12, 2017, 11.30am– 1.00pm (this is a brown bag seminar, attendees are welcome to bring their lunch)
Where: Room 612, Education Building A35
This seminar will not be available online or recorded.
No need to RSVP, just come on the day.


What are the benefits of providing peer feedback (online) and how can it be made even stronger?

Questions of effectiveness and quality of peer feedback and peer assessment have been very actively discussed from the perspective of the receiver of feedback, however the benefits for the provider of feedback messages have received much less attention, at least in Higher Education. But the benefits could be substantial: The process of peer feedback engages students actively in learning, helps develop self-management and judgment, strengthens the capacity for self-assessment, helps develop subject knowledge, enables students to receive feedback faster and promotes social interaction.

Understanding the benefits to the student providing the feedback is becoming more important as the opportunities for engaging in peer tutoring and peer assessment practices explode in the online space. In addition to designed peer learning practices we need to consider informal peer help and peer tutoring episodes.

Peter will give an overview of recent research on the online provision of peer feedback in relation to research on peer feedback and peer tutoring more generally. He will speculate on how learning benefits for the student provider of online feedback might be increased, based on explanatory models for the learning from teaching effect. He’ll end with some thoughts on how technology can help, not only with providing peer feedback, but with the learning that arises from providing feedback.

Prof. Peter Reimann is the co-Director of the Centre for Research on Learning and Innovation (CRLI) at the University of Sydney, (formerly the CoCo Research Centre), in addition to having continued involvement in European Commission funded projects in IT research and development for learning. His primary research areas include cognitive learning with a focus on educational computing and the development of evaluation and assessment methods for the effectiveness of computer-based technologies. Current research includes the analysis of individual and group problem solving/learning processes and possible support by means of ICT, and analysis of the use of mobile IT in informal learning settings (outdoors, in museums, etc.).

Event details:

7 April 2017
12 – 1pm
Room 218, Level 2 South,
Fisher Library


At CRLI, we run a weekly seminar series hosting local and international experts who present research on learning and educational innovation in an informal setting. Seminars run on most Wednesdays in semester.

Upcoming seminars in 2017:
A glance of this year's seminar
12-Apr-2017—Peter Reimann
26-Apr-2017—Andrew Martin
3-May-2017—Kate Thompson
17-May-2017—Andrew Gibson
24-May-2017—Deborah Richards
31-May-2017—Kathryn Bartimote-Aufflick
7-Jun-2017—Learning Analytics Research Group (LARG)
14-Jun-2017—Rachel Wilson
28-Jun-2017—Maria Souza e Silva
2-Aug-2017—Louise Sutherland
9-Aug-2017—Nick Hopwood
20-Sep-2017—Tom Carey
4-Oct-2017—James Dalziel
1-Nov-2017—Learning Analytics Research Group

Please note we are in room 612 of the Education Building. We are always looking for more speakers, topics and ideas. If you would like to suggest a seminar topic, propose a speaker (including yourself) or provide feedback, we would love to hear from you at

When: Wednesdays, 11.30am. Seminars usually run for an hour followed by a 30min Q&A session.
Where: Rm 612 of the Education bldg. (Unless otherwise specified in the seminar's description page).
Brown bag: You are welcome to bring your lunch to these events.

Are you a visual learner? No? Perhaps you are an auditory or a kinaesthetic learner? The idea that people differ in what modality they learn best in, and that knowing this should influence how one is taught is known as "learning styles". This idea is one of the more enduring "neuromyths" in education. Earlier this week, thirty leading researchers in neuroscience, psychology, and education signed a letter to the Guardian strongly condemning learning styles approaches—"No evidence to back the idea of learning styles".

The idea of "learning styles", however, is not the only neuromyth that makes its way around our education system. Paul Howard-Jones reviewed the broader issue of educational neuromyths in the prestigous neuroscience journal Nature Reviews Neuroscience in 2014. One of the findings discussed was that of a survey of practising teachers in five different international contexts. These teachers were asked about various neuromyths and whether they believed them to be true. The table below is taken from this paper, and shows the results of this survey.

Screen Shot 2017-03-15 at 12.06.13 pm.png

And while on the subject of the brain, we were asked a question about how our brain decides which words to use. For example, as I type this now, why am I choosing to use these words, rather than different ones?

First of all, we still have much to learn about the brain, this issue included. However, one generally accepted idea is that our brains work to constantly gather evidence in order to better understand and make predictions about the world our bodies inhabit. This, as various arguments go, is what our brains evolved to do. So, much of our unconscious decision making about which words we use is governed by our brain picking up subtle contextual cues in the environment that bias it toward certain decisions.

Mathematically, neuroscientists sometimes use what is called a drift-diffusion model to analyse this phenomenon and how it might correlate with certain brain signals.

drift model.jpg
A drift-diffusion model. The horizontal axis represents evidence gathered over time and the vertical axis represents nearness to a decision boundary (Zhang & Rowe, 2014)

To make this more concrete, I thought up an analogy on my walk into CRLI this morning. Imagine the situation of two people walking along a footpath towards each other. When deciding who walks on what side, in Australia, we have a leftward bias influenced by our traffic laws. But what happens if these people are already quite close and just happen to be walking on their respective right-hand sides? Our brains, here, recognise it is easier to stick on the side we are already on, and this overrides our natural tendency to veer left. In this instance, our brains are gathering spatial and movement information from what we see and hear to decide where we walk, and this is also influenced by cultural context. Back to the word choice example, I might normally write one word on a blog (cultural and experiential biases) but I might subconsciously choose another one influenced by what I might have been reading that day. At a higher level, it is similar to how we have ideas. For example, my walk this morning influenced the ideas I talked about in this blog.


Howard-Jones, P. A. (2014). Neuroscience and education: Myths and messages. Nature Reviews. Neuroscience, 15(12), 817-824.

Zhang, J., & Rowe, J. (2014). Dissociable mechanisms of speed-accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift-diffusion model. Frontiers in Neuroscience, 8(8), 69.

In the last couple decades, one of the most talked about ideas in education has been that of the "flipped classroom"—see for example In a traditional classroom, we receive a lecture on a topic, then get practice in applying the topic for homework. A flipped classroom "flips" this structure. In other words, we watch a video of a lecture at home and then practice and apply the concept in the classroom. The advantage of a video lecture is that one can pause or rewatch sections of a video if one fails to understand, all without time pressure or peer pressure (e.g. not being confident enough to ask a question). The advantage of 'homework' in the classroom is that the teacher can more actively support the students during formative stages of applying a concept and intervene more immediately when needed.

Despite a growing interest in flipped classroom models of learning, there has been very little research that has empirically evaluated its efficacy. And despite its prominence in the popular consciousness, there are a number of educational researchers not so convinced of its claims to "revolutionise education". So given this, an international team, including CRLI core member Abelardo Pardo, has taken a critical look at this issue in a new paper in the journal The Internet and Higher Education. Working with methodologies from the field of learning analytics, they identify 4 different learning strategies and 5 different student profiles that tend to occur within flipped classroom teaching. Having a finer grain of analysis, as offered here, enables educational researchers and designers to better understand and improve how we might use flipped classroom teaching.

See the original paper for details.

Screen Shot 2017-03-01 at 4.36.18 pm.png
Two representations of student activity patterns for A) students performing above the 90th percentile, and B) students performing below the 25th percentile

And as a bonus piece of good news, Abelardo has just been appointed a "Senior Fellow" of The Higher Education Academy: a professional institution promoting excellence in higher education. The Senior Fellow distinction is given to individuals that demonstrate a thorough understanding of effective approaches to teaching and learning support as a key contribution to high quality student learning.

About the Blog

Research by the University's Centre for Research on Learning and Innovation (CRLI).