Month: September 2020

the manifestation of cognitive overload

ATC -- Frazzled Cartoon Lady

The reactions people have to cognitive overload are varied. Some get angry, some withdrawn, some somewhere in the middle. What is common to all who experience it though is feeling overwhelmed, feeling uncomfortable, feeling frustrated and sometimes feeling worthless. Imposter syndrome can be common.

Understandably, it’s an experience we want to avoid. It can be exhausting.

How students handle it is largely determined by their temperament, which is affected by a multitude of factors. The more obvious reactions are the extremes: poor behaviour, lashing out, belligerence and compete withdrawal. Of course, poor behaviour and withdrawal is more complex than just cognitive overload, but at times it is certainly a factor, and I find it strange that little to no conversation ever discusses improving behaviour in the same way that we endorse the elimination of academic cognitive overload – through incrementally improving cognitive skills. Inculcating new behaviours surely needs the same level of design and commitment? Perhaps less obvious is cognitive overload in students who externally give few clues that they are experiencing it; perhaps they are not reacting because of compliance to the school’s rules or respect for authority, or perhaps because they don’t want to be seen as not understanding what is being taught; peer pressure is huge in all education sectors. Perhaps they are having a difficult time outside of the classroom, most certainly a factor affecting higher education students who may have lost their employment during COVID. Needless to say, cognitive overload reduces learning.

See the source image

A practical and relatively simple solution to mitigate against too much cognitive load is the design of learning sequences that focus on the building of schema that include lots of formative assessment to check learning. Good communication with students also allows you to gauge how students are feeling in their learning, and this can be an extremely useful form of formative assessment too.

It’s not just students who feel it

It’s certainly not just students who experience it . Any time you are under pressure in a new situation you are likely to experience it to some degree as your mind grapples with the new content and searches relevant schema to connect it to: the more the pressure and the fewer the connections, the greater the load. You are likely to experience it when you attend a conference where presentations don’t adhere to multi-media principles, you are likely to experience it in a meeting when you don’t have the relevant background knowledge on a topic being discussed, and you are likely to experience it when you yourself are presenting/teaching and you don’t fully understand or believe in what you are discussing. Of course, the most obvious analogy is when your practice is being observed. All of these examples are the times when you are effectively a student, a novice learner. As an educator, it is important that you reflect on the feeling of cognitive overload and how easily it can occur, and use that knowledge to consider how you design and shape the learning experiences of your students so they experience it less, and learn more.

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger

“ATC — Frazzled Cartoon Lady” by campbelj45ca is licensed under CC BY-SA 2.0

chunking lectures – it’s a bit of a no-brainer

Breaking a lecture up into distinct chunks or sections is a bit of a no-brainer. It is all to do with understanding the implications of cognitive load theory, specifically that the brain can only process a small amount of new information at once. Presenting more information than the brain’s architecture can handle leads to overloading the working memory, and usually a significant decrease in learning.

Breaking your lecture into chunks provides students a chance to process each chunk before new material is presented. Designing opportunities for students to be active (black boxes) in the processing of the content also assists in facilitating the content’s understanding, and eventual transfer into long term memory.

So, here’s a possible live -streamed lecture design that considers cognitive load implications, the need for the student to be active in their learning, and is very manageable for the lecturer. The model can be applied to both live and recorded lectures, but the recorded lecture will need some more specific context discussed, which I will do in another post.

I’ve talked before about the possible mixed-mode future of live lecturing, with it being able to facilitate a breakout room. The below model considers this as a possibility.

lesson segmentrationaletech to assist
introThe lesson begins with a retrieval quiz.
The benefit of retrieval is enormous. It strengthens the memory of key ideas and content. The purpose of this is so the knowledge can be automatically brought to cognition when new learning is presented, without taxing the working memory. The more knowledge the student can draw from, the greater the opportunity to delve into more higher order independent learning, so building students’ schema through retrieval is is a bit of a no-brainer.
The lecturer will place answers on the screen, and spend 2-3 minutes explaining answers if common errors were made. 		Polls
Echo 360
Mentimeter
Quizziz
Canvas quiz
teachingDelivering content.
10-12 min.
Incremental building to application is is a bit of a no-brainer. The lecturer is conscious of the need to present content clearly and simply, very much aware of multimedia principles that promote the efficient encoding of new information. They are also aware of the importance of modelling problem solving and incorporate worked examples into the presentation. Where appropriate, the lecturer connects the new learning to real world applications, not just to make the content relevant, but more so to build the mental patterns and analogies in the students’ schemata.
The lecturer also frequently mentions the reasons why decisions in the teaching are being made so as to strengthen the students’ metacognition.
PPT slides.
Document camera.
Students can take notes in Echo, can raise confusion flag, and ask a question at precise point in either the live stream.
student activityStrengthening understanding
This provides students a chance to take in what has just been presented, and think about the concepts before tehy are presented with more content. Essentially the student is trying to convert the abstract to the concrete. Providing students with the opportunity to complete worked examples, practise solving similarly structured problems, or discussing with a peer possible analogies to the content is valuable at this point in the lecture, and is a bit of a no-brainer. 		Breakout rooms.
Mentimeter open question.
Echo discussions. Canvas discussions.
GoFormative.
teachingDiscussion of last task if necessary – may not be if practising or completing examples.
Delivering content.
10-12 min.
Incremental building to application is a bit of a no-brainer. The lecturer is conscious of the need to present content clearly and simply, very much aware of multimedia principles that promote the efficient encoding of new information. They are also aware of the importance of modelling problem solving and incorporate worked examples into the presentation. Where appropriate, the lecturer connects the new learning to real world applications, not just to make the content relevant, but more so to build the mental patterns and analogies in the students’ schemata.
The lecturer also frequently mentions the reasons why decisions in the teaching are being made so as to strengthen the students’ metacognition.		PPT slides.
Document camera.
Students can take notes in Echo, can raise confusion flag, and ask a question at precise point in either the live stream.
Formative assessmentChecking for learning
A quiz of short answer opportunity to see if what you have presented so far has been understood is is a bit of a no-brainer. The questions also provide another opportunity for a student to process the content and develop a better understanding. 		Questions up on screen.
Zoom polling.
Using Canvas discussions as student answer repository.
Mentimeter.
Quizziz.
teaching Check answers – you may need to pivot the lecture if misconceptions are still prevalent.
Delivering content.
10-12 min.
Incremental building to application is a bit of a no-brainer. The lecturer is conscious of the need to present content clearly and simply, very much aware of multimedia principles that promote the efficient encoding of new information. They are also aware of the importance of modelling problem solving and incorporate worked examples into the presentation. Where appropriate, the lecturer connects the new learning to real world applications, not just to make the content relevant, but more so to build the mental patterns and analogies in the students’ schemata.
The lecturer also frequently mentions the reasons why decisions in the teaching are being made so as to strengthen the students’ metacognition.		PPT slides.
Document camera.
Students can take notes in Echo, can raise confusion flag, and ask a question at precise point in either the live stream.
student activityStrengthening understanding
This provides students a chance to take in what has just been presented, and think about the concepts. Essentially the student is trying to convert the abstract to the concrete. Providing students with the opportunity to complete worked examples, practise solving similarly structured problems, or discussing with a peer possible analogies to the content is valuable at this point in the lecture.		Breakout rooms.
Mentimeter open question.
Echo discussions. Canvas discussions.
GoFormative.
summary Recapping key ideas. Tying the lecture all together: linking it to previous learning and real word contexts. Discussion and questions asking students to link their learning is a great way to draw attention to the key concepts again, and is a bit of a no-brainer. Mentimeter open ended question.

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger

the incredible power of analogy

THE 3 A’S OF KNOWLEDGE TRANSFER: ACQUIRE, ANALOGISE, APPLY PT 2

This is the third instalment of a 4 part series aimed at assisting an educator in designing a sequence of learning that drives towards the ultimate goal of knowledge transfer. The intro post is here and the post on the first stage of developing a context of transfer, acquiring knowledge, is here

PT 2: Analogous examples 

The importance of presenting analogous examples to students to facilitate transfer is made apparent by Gentner and Ratterman (1991), who purport that people learning from a single example of content tend to encode it in a context-specific manner, with the result that later remindings are often based on more obvious surface aspects. Applying knowledge to a new context from only seeing one example of the learning is significantly harder compared to providing an analogy for students, but as Reeves and Weisberg suggest, it is not enough to simply provide a single analogous examples to your students. 

The beginnings of analogical reasoning

 Karl Duncker was a quite remarkable German psychologist.

here

He dedicated considerable energy into exploring how learners approach problem solving, and his thesis on how it happens is here. His candle problem highlighted the notion of Functional Fixedness, which highlighted that once a learner has established a schema for a certain function, it is difficult for the ‘function’ to be applied in another context. Before his tragic death, suicide at 37 years of age, Duncker created the ‘radiation problem’, where he established that learners asked to solve the problem could only do so 10% of the time. It is this very low number that paved the way for considerable research into how to assist the problem solving dilemma of functional fixedness.

Gick and Holyoak are perhaps the most notable of researchers to shed light on how to mitigate functional fixedness. They found that when students were given an analogy to the Duncker tumour problem that had the same underlying structural properties, the number of students who were able to solve the problem rose from 10% to 30%. Incredibly, when they experimented by providing a 2nd analogy prior to exposing students to the radiation problem they found that 80% of students could then solve the problem. When they provided a underlying principle to students, when only a single analogy was used it did not assist students, but when 2 analogies were given, the underlying principle increased student success to 82% with a verbal principle, and 92% with a diagrammatical principle.

Of particular note however, was Gick and Holyoak’s attention to the quality of a student’s schema when applying the analogies to the problem. What they found, consistently, was that when a student presented a good quality schema, found by having students articulate the similarities between analogies, 100% of students were able to solve the problem. This has enormous implications for the need to ensure that a well-developed schema is present when asking students to apply or transfer knowledge into a new context.

It highlights the fact that it is the bank of mental models and patterns that a student has that allows them to search and seek connections from the schema to the new learning context. If they have a good understanding of the general principles of a problem, characterised by identifying the deeper relational structure of a problem, then it is more likely they will be able to see the same structure in a new problem. As Duncker states, ‘one can transpose a solution only when one has grasped its functional value, its general principle, i.e., the invariants from which, by introduction of changed conditions, the corresponding variations of the solution follow each time.’

Gick and Holyoak’s work has been validated by other researchers. Alfieri, Nokes-Malach and Schunn conducted a meta-analytic review of research using comparison examples to assist problem solving, and found conclusive evidence that analogical reasoning using several comparisons benefits the transfer of knowledge. Jacobson et al report that providing students’ opportunity to search for the similarities of analogy structures improves problem solving and transfer capability, and adding to the weight of evidence, Markman and Gentner suggest that directing students to the structural similarities is what eventually builds the schema that the student will use to connect to new learning contexts.

So, analogy greatly assists students in being able to transfer knowledge into a new context. Providing at least 2 analogies and explicitly pointing students to the similarities appears to be the optimal context for developing the relevant schema for transferring and applying knowledge.

How to exploit this ability to transfer and apply knowledge will be the basis of the next post.

References

Language and the career of similarity. Gentner, D., & Rattermann, M. J. (1991). In S. A. Gelman & J. P. B yrnes (Eds.), Perspective on thought and language: Interrelations in development (pp. 225-277).New York: Cambridge University Press.

Learning Through Case Comparisons: A Meta-Analytic Review. Louis Alfieri,Timothy J. Nokes-Malach &Christian D. Schunn. Pages 87-113 | Published online: 20 Apr 2013

Schema abstraction with productive failure and analogical comparison: Learning designs for far across domain transfer. Jacobson, M., Goldware, M., Lai, P. 2020. https://www.sciencedirect.com/science/article/pii/S0959475218301506

Structural Alignment during Similarity Comparisons. Markman, A.B., Gentner, D. (1993). https://www.sciencedirect.com/science/article/pii/S001002858371011X

The role of content and abstract information in analogical transfer. REEVES, L. M., & WEISBERG, R. W. (1994). Psychological Bulletin, 115, 381-400.

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger

ACQUIRING KNOWLEDGE EFFICIENTLY

THE 3 A’S OF KNOWLEDGE TRANSFER: ACQUIRE, ANALOGISE, APPLY PT 1

Pt 1: ACQUIRING KNOWLEDGE EFFICIENTLY 

This is the second instalment of a 4 part series aimed at assisting an educator in designing a sequence of learning that drives towards the ultimate goal of knowledge transfer. The intro post is here.

Concrete to abstract to concrete  

Delivering new learning in concrete terms using concrete examples makes it easier for a student to encode the content. We assimilate or accommodate new information in the context of the patterns, mental models, examples, analogies and experiences that either already were or have been converted to the concrete in our brain, representations that have formed the basis of our schemata. Kolodner describes this reliance on prior learning as case-based scenarios1. The use of models help students to capture the mental patterns required for the knowledge to become understood. It could be suggested that this predisposition to preferring the concrete could lead one to theorise that the concrete represents understanding. However, a necessary process in the incremental development of a schema is the gradual introduction of abstractions, as they instigate the machinations of knowledge transfer.  

A lot of what we teach in higher education is abstract in nature, and necessarily so: ‘The goal of learning an abstract concept is not simply knowledge of one instantiation; it is the ability to transfer, or apply conceptual knowledge to a novel isomorphic situation.’2 However, this creates two issues for learners: abstract content is inherently harder to learn than concrete content, and the links between abstract content and real word applications can often seem distant at best.  

Why is it harder to learn through abstraction? 

It seems our brains are wired to think in concrete terms3. So, if it is harder to learn through abstraction, why not simply avoid it altogether and convert material to the concrete for our students? Well, it all has to do with the facilitation of knowledge transfer. Resnick and Omanson4 assert that ‘learning with concrete objects supports initial understanding of the instructed concept but does not support the transfer of that knowledge to novel but relevant contexts.’ Pashler et al5 concur and extend this: ‘Many experimental laboratory studies and a growing number of classroom based quasi-experiments have found that teaching students about key principles or concepts using only abstract or only concrete representations of those concepts leads to less flexible knowledge acquisition and use than teaching students to recognize and use those key principles across a range of different situations’.   

So how do we get the balance right? It appears that once we have set a foundation using concrete examples, turning to metaphor is the next step. Reece (2003)6 suggests that human analogical reasoning engaged through metaphor-based environments helps learners to incorporate new concepts into their existing mental schema. She advocates the use of metaphors believing them to be ‘especially appropriate when learners are introduced to new, abstract concepts’, and she cites Jonassen, (1981)7 who asserts that metaphor acts as a scaffolding ; that is, ‘a learner structures the to be-learned domain, the target, according to the relational structure of the concrete and more familiar domain, the source domain (see structure mapping theory in Gentner, 1983, 1989; Gentner & Markman, 1997).’ 

In designing effective metaphors, Carroll & Mack8 instruct that ‘Within pedagogical applications, an instructional metaphor source domain can be carefully structured so that it replicates the relational structure of the target domain’. It seems that this deliberate scaffolding of metaphor is consistent with the need to attend to the incremental building of a schema; the metaphor is designed to incrementally build abstraction by initially making connections to relational structures quite close, and then gradually making them more abstract. The student’s schema thereby develops by adding to the bank of patterns contained within.   

But a well-developed schema does not necessarily mean that transfer of learning to new contexts is automatic.  Gentner, Rattermann & Forbus (1993)9 report that ‘people often fail to access prior cases that would be useful, even when they can be shown to have retained the material in memory’. Transfer then, requires a lot more attention to design than just acquiring knowledge. One of the ways to achieve it is through analogous examples.  

That is the base of the next post.

References

  1. Case-Based Reasoning: Kolodner, Janet L; 24 April 2005, The Cambridge Handbook of the Learning Sciences,  https://ebookcentral.proquest.com/lib/adelaide/reader.action?docID=261112&ppg=23

2. Do Children Need Concrete Instantiations to Learn an Abstract Concept?
Jennifer A. Kaminski (kaminski.16@osu.edu), Vladimir M. Sloutsky (sloutsky.1@osu.edu), Andrew F. Heckler (heckler.6@osu.edu) – http://csjarchive.cogsci.rpi.edu/proceedings/2006/docs/p411.pdf 

3. Lawson, A. E., Alkhoury, S., Benford, R., Clark, B. R., & Falconer, K. A. (2000). What kinds of scientific concepts exist? Concept construction and intellectual development in college biology. Journal of Research in Science Teaching, 37(9), 996-1018. 

4. Resnick, L.B., and Omanson, S.F. (1987). Learning to understand arithmetic. In R. Glaser (Ed.),  Advances in instructional psychology (Vol. 3, pp. 41-95). Hillsdale, NJ: Erlbaum.

5.  Pashler, H., Bain, P., Bottge, B., Graesser, A., Koedinger, K., McDaniel, M., and Metcalfe, J. (2007) Organizing Instruction and Study to Improve Stu. dent Learning (NCER 2007-2004). Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ncer.ed.gov

6. Reece, D. 2003. Metaphor and content: An embodied paradigm for learning. Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Curriculum and Instruction (Instructional Technology). 

7. Jonassen, D. H. (1981, April 7). Content treatment interactions: a better design model. Paper presented at the Association for Educational Communication and Technology, Philadelphia, PA.

8. Carroll, J. M., & Mack, R. L. (1999). Metaphor, computing systems, and active learning. International Journal of Human-Computer Studies, 51, 385-403.

9. Gentner, D., Rattermann, M. J., & Forbus, K. D. (1993). The roles of similarity in transfer: Separating retrievability and inferential soundness. Cognitive Psychology, 25, 524-575.

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger

The 3 a’s of knowledge TRANSFER: ACQUIRE, ANALOGISE, APPLY intro

THIS 4 PART BLOG SERIES is created to assist an educator design a sequence of learning that drives towards the ultimate goal of knowledge transfer. Consensus around the notion of transfer in learning is loose to say the least: some deny it’s existence, some accept it but differentiate the types of transfer possible, including near, far, (etc), and others dedicate entire epistemologies to its achievement. But whatever your position, few could deny that a major goal of education is to be able to apply what has been taught in the classroom to a broader context, whether that be work and/or the advancement of community, and so these posts attempt to position you better in being able to design a sequence of learning that strives as much as it is possible to transfer student learning to new contexts.  

INTRO: Towards independent learning and transfer

One approach to facilitating transfer has been to teach students how to learn. The rationale seems sound: if a student understands and practises how to go about learning, then they should be able to do it in a new context, independently. Making a student aware of their metacognition is really important, but unfortunately the resulting pedagogy that most often subsumes this direction is a) inspired by the belief that strength and resilience in learning how to learn comes from the student constructing their own knowledge of the process and b) characterised by immersing students in the context of having to think and find knowledge independently. The ostensible bonus is the potential replacement of an anachronistic teaching practice with a modern 21st century student centered pedagogy.  

But, the focus on a modern pedagogy liberating students from the shackles of the sage on the stage and all the imbalance of power that is associated with it misreads the argument of a large body of work* dedicated to providing caution to the increasing popularity of such a discovery/inquiry pedagogy. The sagacity that you need knowledge in a domain to become proficient in that domain, and more pertinently, that achieving the knowledge is more efficient if an expert scaffolds that journey for the novice, as opposed to the novice trying to find the knowledge themselves, is not driven by an impulsion to maintain a neoconservative agenda, or a thwart on choice or constructivist prerogative**, but ultimately driven by a goal to arrive at independent learning faster.  

But student learning will be stronger if they have found the knowledge themselves, won’t it? 

Interestingly, for such a widely held notion, I can’t find any evidence to support the idea that learning things on your own creates a stronger understanding than learning it from a teacher/peer, except if you are already quite proficient in a given topic/area of learning (reversal effect). Determining then how we teach the novice learner, who I contend, makes up quite a large percentage of the modern undergraduate cohort, to independence, needs a pedagogy that is less emotive and more scientific in its design, and one that is conscious of the reality of a curriculum that is starved of time.  

Standing on the shoulders of giants 

Initiating a context where novice students are expected to find knowledge on their own concomitantly initiates a context where novice students may not make the necessary connections between key ideas for a host of reasons: they may invest too much time in researching irrelevant knowledge, they may not ‘see’ the connections between ideas, they may, as John Sweller states, ‘use general problem-solving strategies such as means-ends analysis when faced with a problem’ and exhaust working memory, or worse, they simply may not engage with the autonomy of the context and do no work. Because much of what we teach is sequential, the consequence of students not arriving where we want them to be in the curriculum is that learning gaps will emerge, and these will have to be addressed in the limited time available. Understandably this ‘extra’ teaching is foregone by most, and this invariably leads to equity issues, with often only the highly motivated, intelligent or culturally literate students able to cope, as they are able to draw from schemata developed from these cultural and mental literacies. But I contend that even those students could be afforded a better more efficient pedagogy, one that scaffolds the acquisition of schema so that more meaningfully higher order thinking can be conducted sooner, and one that facilitates the creative extension of knowledge generated by ‘giant’ scholars.

The imperative of schema 

The reason why it’s inefficient to not scaffold the development of a novice’s knowledge base is highlighted by schema theory. When presented with unfamiliar content, we attempt to either assimilate or accommodate it into our schema, but if the gap between what we have and what is new can’t be connected, the working memory essentially exhausts itself, cognitive dissonance ensues and little learning, if any, happens at that point. In light of encouraging efficient transfer of knowledge, Dunbar’s finding that novices struggle significantly to encode the deeper structures of problems is pertinent: without sufficient analogies in a schema, making a new context consonant with learnt contexts in troublesome.  

The first step in building an appropriate schema is to teach in concrete terms with concrete examples. That is the base of the next post.  

*here are some examples:

Assessment training effects on student assessment skills and task performance in a technology-facilitated peer assessment. Xiongyi Liua and Lan Lib. 2013

Cognitive Load During Problem Solving: Effects on Learning. JOHN SWELLER, University of New South Wales 1988. https://www.sciencedirect.com/science/article/pii/0364021388900237

Constructivism as a theory for teaching and learning. Simply Psychology. McLeod, S. A. (2019, July 17)https://www.simplypsychology.org/constructivism.html

John Hattie on Inquiry Based Learning. https://www.youtube.com/watch?v=YUooOYbgSUg&feature=youtu.be

The Use of Advanced Organisers in the Learning and Retention of Meaningful Verbal Material. Ausubel 1960: https://www.colorado.edu/ftep/sites/default/files/attached-files/ausubel_david_-_use_of_advance_organizers.pdf

What We Know About Learning. Herbert A. Simon. Department of Psychology, Carnegie Mellon University. Source: http://civeng1.civ.pitt.edu/~fie97/simonspeech.html

Why Education Experts Resist Effective Practices (And What It Would Take to Make Education More Like Medicine). Douglas Carnine

Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching . Kirschner, Sweller, Clarke. 2006

**well it may be for some, but for me it’s about efficiency in learning  

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger

Is mixed-mode lecturing the future of HE lecturing?

Picture the setting: instead of the regular face to face lecture of 120 students, there are 40 in front of you and the other 80 are remote. How can a lecturer operate under such conditions, satisfying both contexts at the same time? 

Well, each student is connected to Zoom, face to face students either through a laptop or a phone, and remote students similarly so. The face to face students have a choice – they can watch and hear the lecturer as normal, or watch and listen through the screen, as the remote student would have to do. If slides are presented, then the face to face student likely has an advantage as they can see the lecturer full size and the content on a larger screen, whereas the remote student sees only a thumbnail of the lecturer in the corner of the presentation.   

So, what are some of the advantages of having face to face students being connected via Zoom too – why not just watch and listen as normal? 

100% participation in formative assessment – if everyone has a device then you can assess their understanding at stages of the lecture using polls and quizzes. Beginning each lecture with a retrieval quiz is highly beneficial as it brings back into the minds of your students key ideas from past lectures that you know they need to know – helping them retrieve such content actually helps you too as new concepts will be better understood if students can automatically bring past ‘connected’ ideas into their thinking without taxing the working memory. Half way through a lecture is another good time to formatively check for understanding.

Generally asking lots of questions in a lecture is still good practice, but getting everyone involved is near impossible in a regular lecture context – now technology affords this. Getting more data helps you know if what you’re teaching is being understood.  

Interactions with peers – when appropriate, students can seek clarification from a peer without disturbing the rest of the lecture room. Of course, this should only be encouraged when there is space in the lecture so students aren’t missing key ideas if talking to a peer. You can manage the chat functions to be open to all or so that students can only message you during content delivery. See here for more Zoom engagement advice.

Interactions with the lecturer – potentially, shy students in the lecture theatre can now ask a question to the lecturer, anonymously if they like, via the chat in Zoom. For some, the pressure of not wanting to appear silly by asking a question is huge, and often such students won’t ask, and then move onto the next section of the lesson without clarity on what was just taught. Now everyone can be heard.  

Group work in a lecture – breakout rooms facilitate the option of having students work together to solve problems. At stages in the lecture when chunking is necessary to secure students’ attention, an option may be for students to spend some time to practise what has just been delivered, consider relevant analogies to help strengthen understanding, or collaborate on creative solutions to new problems. Addressing misconceptions or consolidation through practice is probably best done in pairs, whereas groups of 3-5 may be more suited to discussing ideas and analogies rather than practice.  

Black screens can be good – the wonderful Dr David Wilson from Adelaide University provided some valuable insight in this area. There may be several legitimate reasons why a student decides to turn their video off. Of course, the best communicators make their expectations explicit and clear from the beginning, and help students with legitimate screen issues arrive at alternative ways to engage in the lecture, but sometimes a student will turn their screen off because it’s easier to engage passively. We all know that active learning is better than passive learning, but in a large lecture theatre, it can be hard to determine who is and who isn’t active, and time consuming trying to address an individual who pretends not to hear you. Now, the black screen at least gives you a chance at instantly seeing who the passive student is and a chance at addressing their decision. If you’ve made it clear that you prefer the screen on, and that those who can’t should communicate why privately to you, then if  the student simply still refuses to engage when addressed, it’s easy to write down the Zoom name or student number and address it later with a friendly check-in to see if there is anything you can do to help. If the student has used a fake name, well that’s a fair bit harder, but you’d hope that having established high expectations, continually developed the metacognitive abilities of your students, and done so in a really friendly demeanour, then such a student would be in the minority.  

Logistical considerations that may be deemed as disadvantages – it may seem daunting to get all the technology working to facilitate such a learning environment, but it is easier than you might think 

ISSUESOLUTION
Audio feedback from multiple zooms in the lecture theatreStudents would need to be on mute unless asked a question 
Teacher’s zoom camera –  how can it be placed to emulate a real life view?Placed so it captures the teacher’s whole body and gesturing as they move around (movement like in a normal lecture). This means that the camera will be at distance and not so you can only see the person’s head. It may require some configuring with the existing setup so that your camera connects to the console displaying your slides or doc camera, but quite often the lecturer will be distant from the console and using a clicker to move through slides. 
Teacher’s microphone – how would the distanced camera pick up the lecturer’s voice? Lots of lecture rooms have a microphone that is pinned to the lecturer and operates via bluetooth. A room microphone would pose problems of feedback, but if that is the only option, then face to face zoom participants must always have their mic muted and questions and answers  asked in house would need to be repeated by the lecturer for the sake of the remote students – or questions are asked via zoom chat. This is actually not a bad outcome anyway as repeating the question ensures a) everyone heard it, and b) a longer processing time to engage with it.  
Being able to produce worked examples and use a whiteboard to demonstrate problem solvinguse a tablet as the screen share in Zoom where you can draw/write and show your workings. Alternatively, you can use your phone as the screen share and position/suspend it above your working area.
Monitoring the chat effectivelyI would dedicate a section of the lecture where you stop to check for questions. This is surely just good practice anyway.  

Previously perhaps the promotion of such a learning environment may have been frowned upon as a threat to lectures going ahead at all – why would we need to have a live lecture when it can be watched online, at one’s own convenience. Well, it would seem that the average cohort of lecture audience has always contained a mix of those who like and benefit from the in-person ‘live’ experience and those who prefer the remote alternative. Mixed-mode lectures offer the best of both worlds.  

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger