I’ve written before about why I think attention in classrooms is an area that both teachers and researchers should be more interested in. That blog is a few years old now, but I think is still a pretty accurate summary of what we know, given that it remains a fairly neglected research area (major influence on academic trends that I clearly have). I realised this week, however, that I haven’t ever written a slightly more optimistic counterpoint to that piece, detailing what I think teachers can infer from attention research, or at least where the jumping off points might be for teachers to begin exploring ideas in their own practice. Thanks goes to Mark Enser for providing the nudge needed to fill this gap. I’d love to hear other teachers’ strategies for maximising attention (I really recommend this blog from Ian Taylor, on a schoolwide system of ‘Track the Speaker’, for example).
Drawing tentative conclusions from patchy evidence
First, however, a quick note on the mindset I think is needed to interpret and start using some of this evidence. As I have said, evidence on the function of attention in classrooms, and how it might fluctuate, be controlled, or be improved, is very scarce. This is not like retrieval practice (with its hundreds of supporting studies across a range of learning environments). As ‘evidence-based’ practitioners, however, I don’t think that this should automatically mean that we don’t engage with areas where less research has been done. If we agree that no amount research evidence is a guarantee of success in a given classroom, and that evidence therefore represents the starting point in a developmental process rather than the end of the conversation, then we shouldn’t fear starting points that are a little further back. There are lots of reasons why research may not have been done which are nothing to do with the potential benefits it could have for some classrooms. With that in mind, therefore, here are the ‘starting points’ for teachers keen to consider attention more closely in their classrooms, based on my readings of the evidence available.
How distracted are students in the classroom?
Classroom distractions can come in a number of forms. In my PhD I took the not particularly inventive step of asking secondary school age students (Yrs 8-12) what sources had distracted them. Immediately after a regular classroom lesson they estimated the total duration of distraction they had experienced from different sources.
Picture from (Hobbiss, 2019)
I think two main conclusions emerge from this graph. Students are distracted a lot of the time, by a wide variety of sources. This fits with older observational studies which found that elementary students spent between 25% and 50% of instructional time off-task (Karweit & Slavin, 1981). This clearly has implications for us as teachers trying to design and manage our classrooms. In my thesis we also found that performance on a laboratory task of attention control could predict these self-reports of lesson distraction (Hobbiss, 2019), so this does seem to be a feature that is related to the individual’s ability to control their attention, rather than simply reflect different lesson environments.
But what are the consequences of classroom distraction? And what should we as teachers do about it? Below are my ‘best bets’ for the classroom. Remember, this is only in terms of maximising attention control. You may use other techniques in order to achieve other goals. That’s entirely your prerogative as an informed teacher.
Background classroom noise has been found to negatively impact reading comprehension and word learning in adolescents (Connolly et al., 2019) and may have some negative effects on creativity in children, depending on their initial attention control skills (Massonnié, Rogers, Mareschal, & Kirkham, 2019). It’s not clear whether noise needs to be above a certain level though (Connolly et al., 2019 found some suggestions of this), so it’s possible that relatively quiet background hubbub may not be too harmful, at least to some people. However, given how much we know that attention skills vary across the population (admittedly from numerous studies on adults but I see no reason why such variation would not also be present in childhood), it’s not clear at what level some noise may start to interfere for individuals with reduced attention control.
Best bet? If you have a task that will require any level of close concentration or careful thought, it is best to aim to have students do that in as quiet an environment as possible.
The function, and usefulness, of classroom displays is a source of perennial debate. High density of visual displays have been linked to increased time off task and reduced learning (Fisher, Godwin, & Seltman, 2014; Hanley et al., 2017; Rodrigues & Pandeirada, 2018), though these studies don’t always manage to accurately recreate classroom environments very satisfactorily. Also bear in mind that these studies don’t account for the probability that the distracting effect of displays over time will reduce as we become used to them (distractions in general are less potent the more predictable they are and the more habituated we are to them; Marsh, Röer, Bell, & Buchner, 2014), and as far as I know there is no research on displays that looks at their effect when they are regularly used as part of classroom routines, in which case they could easily become very useful. I would also note that in my thesis results (see graph above), displays were consistently the least reported source of distraction, so it may not be that they are actually the most pressing issue. Still, marginal gains are always worth looking for.
Best bet? I have removed all displays from the front half of my classroom (it was interesting to recently visit Heathfield CC in Sussex, where they have taken a similar approach). I would only make a display visible to the class if I knew that I would be referring to it regularly as part of a classroom routine.
The use of technology is an increasing feature of school classrooms, and already a ubiquitous one in higher education, where this has been more studied. Admittedly, we have to be careful in drawing links between studies done in university settings and schools (where the types of technology, programs available and functionality of the devices may be much more carefully regulated). However, there are still lessons to be drawn I think. Non-academic technology use (e.g. social networking, instant messaging, emailing or texting) has been consistently associated with poorer learning (e.g. Courage, Bakhtiar, Fitzpatrick, Kenny, & Brandeau, 2015; Flora Wei, Ken Wang, & Klausner, 2012; Kuznekoff & Titsworth, 2013; Risko, Buchanan, Medimorec, & Kingstone, 2013; Wood et al., 2012). Notably, the harmful effects of engaging in non-academic technology use during a learning session are not limited to the individual, but also negatively impact the learning of others for whom the screens are in their eyeline (Glass & Kang, 2018; Sana, Weston, & Cepeda, 2013). These negative effects are partially the result of distraction (e.g. by notifications, pop-ups etc) and partially through the illusion that we can multitask on a number of apps/tabs/functions at the same time (which many devices are designed to create). I’ve written before how the idea that any of us can efficiently multitask is an illusion, one that has increasing relevance and importance as technology steadily encroaches into ever younger classrooms.
Best bet? I’m not against tech in classrooms per se. Here’s an example I really like – the ‘Compare and Learn’ app. It can be an amazing tool. However, from an attention perspective there are clearly serious potential pitfalls. If I use tech in the classroom, I try to do so in an extremely tightly structured context, ideally using devices designed for educational purposes and so lacking many of the more diverting features often present on laptops, tablets and phones. I would certainly try to avoid very open-ended tasks such as simply ‘internet research’, as these are likely to open up far more opportunities for attention to be diverted. In general, even in my 6th form classes I also don’t allow note taking on laptops (tech distraction is associated with reduced note taking in general). If some students always have laptops or other technological learning aids and most of the class don’t then I try to seat them in a place where the screen will be least likely to distract others (e.g. on the back row)
Tricky one this. Partly because we don’t even know it’s happening half the time, and partly because there doesn’t seem to be much that we can do about it even if we know it’s going on! There’s also some evidence that in environments where external distractions have been minimised (such as silent study rooms), distraction by mindwandering may increase (Robison & Unsworth, 2015), so simply eliminating distractions may not be a silver bullet for attention after all! It’s also amazing how prevalent mindwandering seems to be – nearly 10% of the time in my results from secondary classrooms (see above) and some studies have found it reported on up to 40% of the probes that they deliver! Some evidence does show a potential way forward, though perhaps not a clear one. For example, self-reported interest in the topic of study has frequently been found to be related to reduced mindwandering rates (Kane et al., 2017; Lindquist & McLean, 2011; Unsworth & McMillan, 2013). In my thesis I found that interest in the topic was significantly related to reductions in all distractions, but mindwandering most strongly. Theoretical models of mindwandering also predict that task engagement should be negatively associated with mindwandering (Smallwood & Andrews-Hanna, 2013; Smallwood & Schooler, 2015)
Best bet? Mindwandering can hopefully be reduced by increasing positive student engagement with the learning material or interest in the topic. Easy to say I know….!
It is sometimes claimed that teachers should limit their amount of ‘teacher talk’ in lessons. One attention related justification for this is often that student attention spans are no longer than ten minutes, followed by a significant drop off in attention. The ten minute claim seems to stem from two studies from the 1970s. Johnstone and Percival (1976) conducted an observational study during undergraduate chemistry lectures in which observers made notes of physical signs of inattention such as diversions in gaze. They found that initial breaks in attention occurred after approximately 10-18 minutes. Even more commonly cited is a review by Hartley and Davies (1978), which is odd as this is actually a review of note taking in lectures. Even more oddly, the decline they noted was most common in the last ten minutes of a lecture rather than after the first. Meanwhile other evidence was finding contradictory results, for example Stuart and Rutherford (1978) actually found that medical student attention peaked around 10–15min into the lecture, although it but waned considerably thereafter. More recent research and reviews have consistently found that variations in attention do not follow the simple 10 minute rule. Within a general trend for decreased attention over time (usually over an hour’s lecture in a university setting), attention fluctuates considerably both within and between individuals, and dependent on other environmental variables such as the topic, student interest, instructional format and so on (see e.g. Bradbury, 2016; Bunce, Flens, & Neiles, 2010; Wilson & Korn, 2007). This more recent work has also demonstrated that individual differences in attention are far more important than any arbitrary ‘cut-off’ at which class attention might wane.
Best bet? If there’s something that you really need to explain clearly, and that explanation (or the story you want to tell) takes more that 10 minutes, then go for it (though attention is likely to wane after too long). It might be a good idea to at least try to break up your explanations though, partly if you suspect that individuals in your class may struggle with attention control, and also partly because…
In a recent article for the TES, Jenni Kemp and I outlined findings on an effect known as the ‘forward testing effect’. The forward testing effect involves the improved learning of new material after a test (i.e. material presented after the retrieval opportunity). In other words, providing recall opportunities seems to prime us to learn new information more successfully. There are a few possible reasons for this but the simplest may just be that the expectation of future testing makes learners concentrate a bit harder on new material. Experiments into this area often involve ‘interpolated testing’ designs, where small tests are interpolated throughout a learning session. Interpolating tests in this way seems to lead to improved learning of new material (Chan, Meissner, & Davis, 2018; Pastötter & Bäuml, 2014; Yang, Potts, & Shanks, 2018), but it has also been found that tested groups report improved attention (in the form of lower levels of mindwandering; Jing, Szpunar, & Schacter, 2016; Szpunar, Khan, & Schacter, 2013). Interestingly, in my own thesis, I managed to replicate a reduction in mindwandering after interpolated testing (in a university seminar), but the effect did not extend to external distractions (which weren’t significantly reduced by the testing; Hobbiss, 2019). This might be because of something as simple as the sample size or the range of distractions measured not being large enough, or it might be something more fundamental. We don’t know at present.
Best bet? Even if it only impacts on mindwandering, this would still seem a worthwhile aim (especially given how prevalent mindwandering tends to be in everyday life). I now think very carefully about whether any new information that I deliver to a class can be chunked into segments (a segment has in other educational literature been defined as “a block of time with a particular focus or intention”; Burns & Anderson, 1987, and I find that an helpful definition), and whether recall opportunities could be provided after each segment. The recall task does not necessarily need to be testing knowledge of the segment just completed (it could be for previous segments, or the lesson so far as a whole, or even other relevant synoptic material). I try not to stray too far from the main topic though, to avoid confusion.
Avoiding cognitive overload
Increased cognitive load makes people more susceptible to distraction (Carmel, Fairnie, & Lavie, 2012; Lavie, 2000, 2010; Linnell & Caparos, 2011), as a loaded working memory has more trouble maintaining goal focus and prioritising information. However, ‘measuring’ the load imposed by any situation is hard as it will vary for each individual, as will their ability to cope with it.
CAVEAT: I’m using ‘cognitive load’ in the sense it’s generally used in psychology and neuroscience literature, which doesn’t differentiate between ‘types’ of cognitive load as in Sweller’s Cognitive Load Theory in education. I know that there are lots of enthusiastic debates about the validity and value of CLT. I’m not worried about those here. My concern is the implications of cognitive load (in general) for attention.
Best bet? I now aim to break my instructions down into chunks of one or two steps at a time. Dual-coding instructions or information can also help reduce cognitive load. I am also a big fan of graphic organisers, which allow schemas to be laid out visually, and let me as a teacher pre-structure aspects of a student’s thought process. All these should prevent cognitive overload, and hopefully positively impact attention.
Any potent sources of distraction that I’ve missed? Please do get in touch.
Bradbury, N. A. (2016). Attention span during lectures: 8 seconds, 10 minutes, or more? Advances in Physiology Education, 40(4), 509–513. https://doi.org/10.1152/advan.00109.2016
Bunce, D. M., Flens, E. A., & Neiles, K. Y. (2010). How long can students pay attention in class? A study of student attention decline using clickers. Journal of Chemical Education, 87(12), 1438–1443. https://doi.org/10.1021/ed100409p
Burns, R. B., & Anderson, L. W. (1987). The Activity Structure of Lesson Segments. Curriculum Inquiry, 17(1), 31–53. https://doi.org/10.1080/03626784.1987.11075276
Carmel, D., Fairnie, J., & Lavie, N. (2012). Weight and see: loading working memory improves incidental identification of irrelevant faces. Frontiers in Psychology, 3, 286. https://doi.org/10.3389/fpsyg.2012.00286
Chan, J. C. K., Meissner, C. A., & Davis, S. D. (2018). Retrieval Potentiates New Learning : A Theoretical and Meta-Analytic Review. Psychological Bulletin, 144(11), 1111–1146.
Connolly, D., Dockrell, J., Shield, B., Conetta, R., Mydlarz, C., & Cox, T. (2019). The effects of classroom noise on the reading comprehension of adolescents. The Journal of the Acoustical Society of America, 145(1), 372–381. https://doi.org/10.1121/1.5087126
Courage, M. L., Bakhtiar, A., Fitzpatrick, C., Kenny, S., & Brandeau, K. (2015). Growing up multitasking: The costs and benefits for cognitive development. Developmental Review, 35, 5–41. https://doi.org/10.1016/j.dr.2014.12.002
Fisher, A. V., Godwin, K. E., & Seltman, H. (2014). Visual Environment, Attention Allocation, and Learning in Young Children: When Too Much of a Good Thing May Be Bad. Psychological Science, 25(7), 1362–1370. https://doi.org/10.1177/0956797614533801
Flora Wei, F. Y., Ken Wang, Y., & Klausner, M. (2012). Rethinking College Students’ Self-Regulation and Sustained Attention:Does Text Messaging During Class Influence Cognitive Learning? Communication Education, 61(3), 185–204. https://doi.org/10.1080/03634523.2012.672755
Glass, A. L., & Kang, M. (2018). Dividing attention in the classroom reduces exam performance. Educational Psychology, 0(0), 14. https://doi.org/10.1080/01443410.2018.1489046
Hanley, M., Khairat, M., Taylor, K., Wilson, R., Cole-fletcher, R., & Riby, D. (2017). Classroom displays- attraction or distraction? Evidence of impact on attention and learning from children with and without autism. Developmental Psychology, May 4.
Hartley, J., & Davies, I. K. (1978). Note‐taking: A critical review. Innovations in Education & Training International, 15(3), 207–224. https://doi.org/10.1080/0033039780150305
Hobbiss, M. H. (2019). Attention, Mindwandering and Mood: relating personal experiences in daily life and in the classroom to laboratory measures. UCL, UK.
Jing, H. G., Szpunar, K. K., & Schacter, D. L. (2016). Interpolated testing influences focused attention and improves integration of information during a video-recorded lecture. Journal of Experimental Psychology: Applied, 22(3), 305–318. https://doi.org/10.1037/xap0000087
Johnstone, A., & Percival, F. (1976). Attention Breaks in Lectures. Education in Chemistry. Retrieved from https://eric.ed.gov/?id=EJ136799>
Kane, M. J., Smeekens, B. A., von Bastian, C. C., Lurquin, J. H., Carruth, N. P., & Miyake, A. (2017). A Combined Experimental and Individual-Differences Investigation Into Mind Wandering During a Video Lecture. Journal of Experimental Psychology-General, 146(11), 1649–1674. https://doi.org/10.1037/xge0000362
Kuznekoff, J. H., & Titsworth, S. (2013). The Impact of Mobile Phone Usage on Student Learning. Communication Education, 62(3), 233–252. https://doi.org/10.1080/03634523.2013.767917
Lavie, N. (2000). Selective attention and cognitive control: Dissociating attentional functions through different types of load. In S. Monsell & J. Driver (Eds.), Control of cognitive processes : Attention and Performance XVIII (p. 779). MIT Press. Retrieved from https://books.google.co.uk/books?hl=en&lr=&id=kO_baYlSVbwC&oi=fnd&pg=PA175&dq=Selective+attention+and+cognitive+control:+Dissociating+attentional+functions+through+different+types+of+load.+In+Attention+and+performance+&ots=ps5GMKknT3&sig=4FcFWfVmkZCtHgQCf
Lavie, N. (2010). Attention, Distraction, and Cognitive Control Under Load. Current Directions in Psychological Science, 19(3), 143–148. https://doi.org/10.1177/0963721410370295
Lindquist, S. I., & McLean, J. P. (2011). Daydreaming and its correlates in an educational environment. Learning and Individual Differences, 21(2), 158–167. https://doi.org/10.1016/j.lindif.2010.12.006
Linnell, K. J., & Caparos, S. (2011). Perceptual and cognitive load interact to control the spatial focus of attention. Journal of Experimental Psychology: Human Perception and Performance, 37(5), 1643–1648. https://doi.org/10.1037/a0024669
Marsh, J. E., Röer, J. P., Bell, R., & Buchner, A. (2014). Predictability and distraction: Does the neural model represent postcategorical features? PsyCh Journal, 3(1), 58–71. https://doi.org/10.1002/pchj.50
Massonnié, J., Rogers, C. J., Mareschal, D., & Kirkham, N. Z. (2019). Is Classroom Noise Always Bad for Children? The Contribution of Age and Selective Attention to Creative Performance in Noise. Frontiers in Psychology, 10(February), 1–12. https://doi.org/10.3389/fpsyg.2019.00381
Pastötter, B., & Bäuml, K. H. T. (2014). Retrieval practice enhances new learning: The forward effect of testing. Frontiers in Psychology, 5(APR), 1–5. https://doi.org/10.3389/fpsyg.2014.00286
Risko, E. F., Buchanan, D., Medimorec, S., & Kingstone, A. (2013). Everyday attention: Mind wandering and computer use during lectures. Computers & Education, 68, 275–283. https://doi.org/10.1016/j.compedu.2013.05.001
Robison, M. K., & Unsworth, N. (2015). Working Memory Capacity Offers Resistance to Mind-Wandering and External Distraction in a Context-Specific Manner. Applied Cognitive Psychology, 690(July), 680–690.
Rodrigues, P. F. S., & Pandeirada, J. N. S. (2018). When visual stimulation of the surrounding environment affects children’s cognitive performance. Journal of Experimental Child Psychology, 176, 140–149. https://doi.org/10.1016/j.jecp.2018.07.014
Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education, 62, 24–31. https://doi.org/10.1016/j.compedu.2012.10.003
Smallwood, J., & Andrews-Hanna, J. R. (2013). Not all minds that wander are lost: the importance of a balanced perspective on the mind-wandering state. Frontiers in Psychology, 4, Article 441. https://doi.org/10.3389/fpsyg.2013.00441
Smallwood, J., & Schooler, J. W. (2015). The Science of Mind Wandering: Empirically Navigating the Stream of Consciousness. Annual Review of Psychology, 66(1), 487–518. https://doi.org/10.1146/annurev-psych-010814-015331
Szpunar, K. K., Khan, N. Y., & Schacter, D. L. (2013). Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences, 110(16), 6313–6317. https://doi.org/10.1073/pnas.1221764110
Unsworth, N., & McMillan, B. D. (2013). Mind wandering and reading comprehension: Examining the roles of working memory capacity, interest, motivation, and topic experience. Journal of Experimental Psychology: Learning Memory and Cognition, 39(3), 832–842. https://doi.org/10.1037/a0029669
Wilson, K., & Korn, J. H. (2007). Attention During Lectures: Beyond Ten Minutes. Teaching of Psychology, 34(2), 85–89. https://doi.org/10.1080/00986280701291291
Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers and Education, 58(1), 365–374. https://doi.org/10.1016/j.compedu.2011.08.029
Yang, C., Potts, R., & Shanks, D. R. (2018). Enhancing learning and retrieval of new information: a review of the forward testing effect. Npj Science of Learning, 3(8), 1–9. https://doi.org/10.1038/s41539-018-0024-y