AI education

Why I think that AI-based grading in education is inevitable.

A few days ago I commented on an article that discusses the introduction of AI into education and why teachers shouldn’t worry about it. I also said that AI for grading was inevitable because it would be cheaper, and more reliable, fair and valid than human beings. I got some pushback from Ben on Twitter and realised that I was making several assumptions in my post so I’ve written this post to clarify some of what I said. I also wanted to use this post to test my assumptions around the claims I made, so it’s a bit longer than usual since I’m “thinking out loud” and trying to justify each point.

First, there are the 4 claims I make for why I think that AI-based assessment of things like essays is inevitable:

  • AI will be cheaper than human beings.
  • AI will be more reliable than human beings.
  • AI will be more fair than human beings.
  • AI will be more valid than human beings.

Cheaper: Over the past 60 years or so we’ve seen fairly consistent improvements in power, efficiency and speed at increasingly lower costs. Even if we assume that Moore’s Law is bottoming out we’ll still see continued progress in cost reduction because of improvements in programming techniques, purpose-built chips and new technologies like quantum computers. This is important because, like any industry, education works on a budget. If a university can get close to the same outcomes with a significant reduction in cost, they’ll take it. Software vendors will offer the “essay grading” module that can be integrated into the institutional LMS and the costing will be such that universities would be crazy not to at least pilot it. And my thinking is that it’ll become very clear, very quickly that a significant part of essay grading is really very simple for machines to do. Which brings me to the next claim…

More reliable: A lot of essay grading boils down to things that are relatively simple to programme into a system. For example, spelling is largely a problem that we’ve solved (barring regional inconsistencies) and can therefore express as system of rules. These rules can be coded into an algorithm, which is why spell-checking works. Grammatical structure is also generally well-understood, with most cultures having concepts like nouns, verbs, adjectives, etc., as well as an understanding of how these words are best positioned relative to each other to enhance readability and understanding. Whether we use prescriptive rules (“we should do this”) or descriptive rules (“we actually do this”) matters less than knowing what set of rules we’ll use for the task at hand. It seems reasonable that physiotherapy lecturers could tune an algorithm with a slider, specifying that grammatical structure is less important for their students (i.e. lower scores wrt prescriptive rules are OK) while an English lecturer might insist than their students must score higher on how words should be used. Referencing formatting is also easy to code with a series of rules, as well as the idea that knowledge claims should be supported with evidence. And related to this is the idea that machines are getting better at identifying passages of text that are simply copied from a source. And I think it’s reasonable to assert that a computer can count more quickly, and more reliably, than a person. Of course this doesn’t take into account things like creativity but I’ll get to that. For now, we should at least grant that an AI could plausibly be more reliable than a human being (i.e. it assesses the same things in the same way across multiple examples) when it comes to evaluating things like spelling, grammatical structure, essay structure, referencing, and plagiarism. And machines will do this consistently across tens of thousands of students.

Fairer: Human beings are inherently unfair. Regardless of how fair we think we’re being, there are some variables that we simply can’t tune because we’re not even aware that they’re affecting us. There’s evidence that we’re more strict when we’re hungry or when we’re angry with a partner, and that also we’re influenced by the gender of person we’re grading, the time of day, etc. We’re also affected by sequencing; my grading of the essays I read later are influenced by the earlier examples I’ve seen. This means that a student’s grade might be affected by where in the pile their script is lying, or by their surname if the submission is digital and sorted alphabetically. It may be literally true (warning: controversial opinion coming up) that a student’s mark is more strongly influenced by my current relationship with my children than by what they’ve actually written. Our cognitive biases make it almost impossible for human beings to be as fair as we think we are. And yes, I’m aware that biases are inherent to machine learning algorithms as well. The difference is that those kinds of biases can be seen and corrected, whereas human bias is – and is likely to remain – invisible and unaccountable.

More valid: And finally there’s the issue of validity; are we assessing what we say we’re assessing? For essays this is an important point. Essays are often written in response to a critical question and it’s easy for the assessor to lose sight of that during the grading process. Again, our biases can influence our perceptions without us even being aware of them. A student’s reference to a current political situation may score them points (availability bias) while another, equally valid reference to a story we’re not aware of wouldn’t have the same valence for the assessor. Students can tweak other variables to create a good impression on the reader, none of which are necessarily related to how well they answer the question. For example, even just taking a few minutes to present the essay in a way that’s aesthetically pleasing can influence an assessor, never mind the points received for simply following instructions on layout (e.g. margin size, line spacing, font selection, etc.). When you add things like the relationship between students and the assessor, you start to get a sense for how the person doing the grading can be influenced by many other factors besides the students’ ability to answer the essay question.

OK, so that’s why I think that the introduction of AI for grading – at least for grading essays – is inevitable. However, I’m aware that doesn’t really deal with the bulk of the concerns that Ben raised. I just wanted to provide some context and support for the initial claims I made. The rest of this post is in response to the specific concerns that Ben raised in his series of tweets. I’ve combined some of them below for easier reference.

Can we be sure [that AI-based grading of assessment] is not a bad thing? Is it necessarily fairer? Thinking about the last lot of essays I marked, the ones getting the highest grades varied significantly, representing different takes on a wide ranging and pretty open ended topic. As markers we could allow some weaknesses in an assignment that did other things extremely well and showed independence of thought. The ones getting very good but not excellent grades were possibly more consistent, they were polished and competent but didn’t make quite the same creative or critical jump.

I think I addressed the concern about fairness earlier in the post. I really do think that AI-based grading will be more fair to students. There’s also the argument about how the range of examples with the highest grades tend to be quite different. This is a good thing and represents the kinds of open-ended response to questions that demonstrates how students can use their imagination to construct wide-ranging, unanticipated responses to difficult questions. I think that this would be addressed by the fact that AI-based systems are trained on tens of thousands of examples, all of which are labelled by human assessors. Instead of the system being narrowly constrained by the algorithm, I think that algorithms will open up the possible space of what “good” looks like. While I’m often delighted with variation and creative responses from students, not all of my colleagues feel the same way. An AI-based grading system will ensure that, if we highlight “creativity” as an attribute that we value in our assessments, individual lecturers won’t have as much power to constrain its development. And AI systems will also be able to “acknowledge” that some areas of the students’ submissions are stronger than others, and will be able to grade across different criteria (for example, the output might look like: “student’s ability to follow instructions is “excellent”, language – especially grammar – can be improved, ability to develop an argument from initial premises is “good”, etc.”).

How will AI marking allow for the imaginative, creative assignments and avoid a cycle of increasingly standardised and sanitised assignments as students work out how to please the algorithm?

My first response to this is: how do we “…avoid a cycle of increasingly standardised and sanitised assignments as students work out how to please the lecturer?” And then there’s also the progress being made in “creative expressions” of AI-based systems; art (see here and here), music (see here, here, and here), and stories/poems (see here, and here). You can argue that an AI that uses human artifacts to generate new examples is simply derivative. But I’d counter by saying almost all human-generated art is similarly derivative. There are very few people who have developed unique insights that shift how we see the world at some fundamental level. You could also argue that some of these platforms aren’t yet very good. I’d suggest that they will only ever get better, and that you can’t say the same for people.

Is it fairer to aim for consistency of input/output or to allow for individual interpretations of an assignment? What, at heart is the point of assessment in higher education – consistent competence or individual critical thought?

Also who influences the algorithm? Is it on an institutional basis or wider? Is it fairer to allow for varied and localised interpretations of excellence or end up making everyone fit to one homogenous standard (we can guess which dominant cultural norms it would reflect…)

This is an excellent point and the main reason for why I think it’s incumbent on lecturers to be involved in the development of AI-based systems in education. We can’t rely on software engineers in Silicon Valley to be solely responsible for the design choices that influence how artificial intelligence should be used in education. I expand on these ideas in this book chapter (slideshow summary here).

On the whole I think that Ben has raised important questions and agree that these are valid concerns. For me, there are three main issues to highlight, which I’d summarise like so:

  1. There is a tension between creating assignments that enable open-ended (and therefore creative) student responses and those that are more closed, pushing students towards more standardised submissions. Will AI-based grading systems be able to deal with this nuance?
  2. There is a risk that students might become more concerned with gaming the system and aiming to “please the algorithm”, resulting in sanitised essays rather than imaginative and creative work. How can we avoid this “gaming the system” approach?
  3. There is a bias that’s built into machine learning which is likely to reflect the dominant cultural norms of those responsible for the system. Are we happy to have these biases influence student outcomes and if not, how will we counter them?

Looking back, I think that I’ve presented what I think are reasonable arguments for each of the points above. I may have misunderstood the concerns and I’ve definitely left out important points. But I think that this is enough for now. If you’re a university lecturer or high school teacher I think that the points raised by Ben in his tweets are great starting points for a conversation about how these systems will affect us all.

I don’t think that the introduction of AI-based essay grading will affect our ability to design open-ended assessments that enable student creativity and imagination. We’ve known for decades that rules cannot describe the complexity of human society because people – and the outcomes of interactions between people – are unknowable. And if we can’t specify in advance what these outcomes will look like, we can’t encode them in rules. But this has been the breakthrough that machine learning has brought to AI research. AI-based systems don’t attempt to have “reality” coded into them but rather learn about “reality” from massive sets of examples that are labelled by human beings. This may turn out to be the wrong approach but, for me at least, the argument for using AI in assessment is a plausible one.

AI education

Comment: Teachers, the Robots Are Coming. But That’s Not a Bad Thing.

…that’s exactly why educators should not be putting their heads in the sand and hoping they never get replaced by an AI-powered robot. They need to play a big role in the development of these technologies so that whatever is produced is ethical and unbiased, improves student learning, and helps teachers spend more time inspiring students, building strong relationships with them, and focusing on the priorities that matter most. If designed with educator input, these technologies could free up teachers to do what they do best: inspire students to learn and coach them along the way.

Bushweller, K. (2020). Teachers, the Robots Are Coming. But That’s Not a Bad Thing. Education Week.

There are a few points in the article that confuse rather than clarify (for example, the conflation of robots with software) but on the whole I think this provides a useful overview of some of the main concerns around the introduction of AI-based systems in education. Personally, I’m not at all worried about having humanoid (or animal-type) physical robots coming into the classroom to take over my job.

I think that AI will be introduced into educational settings more surreptitiously, for example via the institutional LMS in the form of grading assistance, risk identification, timetabling, etc. And we’ll welcome this because it frees us from the very labour intensive, repetitive work that we all complain about. Not only that but grading seems to be one of the most expensive aspects (in terms of time) of a teacher’s job and because of this we’re going to see a lot of interest in this area by governments. For example, see this project by Ofqual (the UK teaching standards regulator) to explore the use of AI to grade school exams.

In fact, I think that AI-based assessment is pretty much inevitable in educational contexts, given that it’ll probably be (a lot) cheaper, more reliable, fair, and valid than human graders.

Shameless self-promotion: I wrote a book chapter about how teachers could play a role in the development of AI-based systems in education, specifically in the areas of data collection, teaching practice, research, and policy development. Here is the full-text (preprint) and here are my slides from a seminar at the University of Cape Town where I presented an overview.

leadership teaching

Comment: Why South Africa will find it hard to break free from its vicious teaching cycle

There are standards that professionalise teaching and standards that simply manage teachers. While standards which professionalise create cultures of collegiality, expertise and pride among teachers, standards that manage can leave them feeling brow-beaten, untrusted, and demotivated.

Robinson, N. (2019). Why South Africa will find it hard to break free from its vicious teaching cycle. The Conversation.

While the article refers specifically to the primary and secondary teaching context in South Africa, the principles are relevant for a wide range of international higher education and professional contexts as well. The article differentiates between two types of standardisation; professionalisation and management.

Standards that aim to professionalise an activity invariably lead to virtuous cycles. From the article “…teaching [in Finland] is a prestigious and attractive profession which recruits the brightest and most motivated school graduates, who don’t require continual monitoring and oversight. Teachers instead enjoy professional autonomy; they are trusted in key decisions about their teaching and professional development.” You can easily see how this applies to any other profession as well when professionalisation standards are being applied i.e. the standards open up spaces and encourage autonomy as part of trusting relationships.

In contrast, management standards (especially when presented under the pretext of developing professionalism), can lead to vicious cycles. In these situations “…governments take it upon themselves to hold teachers accountable. Standards are used to manage teachers, and to protect students from the worst educators through supervisory surveillance and control. Invariably, the relationship between teacher unions and governments becomes antagonistic and generates feelings of fear and mistrust.” You can see how this could play out in the context of professional organisations tasked with developing cultures of professionalism. Instead of opening up spaces by trusting and supporting people who can make their own choices, organisations may use management standards that aim to close down space and control the people within them.

We need to ask if the standards we’re being asked to meet are aimed at developing cultures of professionalism, or whether they’re simply being used to manage us. One way of determining which standards are being used in your context is to ask how much autonomy you have to make decisions about the work you do.

AI education

Robots in the classroom? Preparing for the automation of teaching | BERA

Agendas around AI and education have been dominated by technology designers and vendors, business interests and corporate reformers. There is a clear need for vigorous responses from educators, students, parents and other groups with a stake in public education. What do we all want from our education systems as AI-driven automation becomes more prominent across society?

Source: Robots in the classroom? Preparing for the automation of teaching | BERA

We need teachers, clinicians, and clinician educators involved in the process of designing, developing, implementing and evaluating AI-based systems in the higher education and clinical context. As long as the agenda for 21st century education and clinical care is driven by corporate interests (and how could it not, given the enormous commercial value of AI), it’s likely that those responsible for teaching the next generation of health professionals will be passive recipients of algorithmic decision-making rather than empowered participants in their design.


Accepting the default configuration

In almost every situation we come across in learning, we accept the default configuration. It’s not because we’re lazy but probably that we’re not even aware that alternative configurations exist. The first time this came to my attention was when I realised in the late 1990s that Windows was not the only computer operating system that existed. Not only were there other options but those options were – IMO – superior in almost every way.

We see the same thing in the default keyboard layout. The QWERTY configuration is not the optimal keyboard layout. It was created to slow typists down because the keys on the typewriters they were typing on jammed. The QWERTY keyboard configuration has been with us ever since. It’s called dominant design, the idea that certain design configurations are common, not because they are the best of competing alternatives, but because of a choice that someone has made.

The problem with dominant design is that almost all innovation is aimed at improving the dominant design rather than exploring competing alternatives. Think about the learning management system. It’s very hard to argue that this is the optimal online learning environment, nor is it a very good content management system. And yet, almost all effort at improving online learning is aimed at making the LMS better. Wouldn’t it be better to invest our time, energy and money into creating something better?

If you’re reading this you probably spend a lot of time writing and you probably use Microsoft Word. You probably use it because it came installed on the computer you’re using and you may not be aware that there are many other options for word processing. You probably type your documents in Calibri because that’s what Microsoft decided to set as the default. This isn’t an inherently bad thing but it has consequences. The fact that you type a document using the default configuration means that your document won’t display accurately on my computer because I don’t run Windows, I don’t have Word, and I don’t have Calibri installed. Is this your fault? Of course not. You just accepted the defaults.

What about classrooms? The configuration of things in space influences the nature of the interactions we can have in those spaces. In the classroom desks and chairs are almost always set up in rows. There is a front and back to the room. The teacher stands in the front. The students sit, facing the teacher. There is a power relationship that is set up by how we configure our bodies in space. Who stands and who sits. Who sits where? Who has to raise their hand to speak? Why have we decided to keep this up? These defaults determine how we teach. Is it because this configuration of physical space represents the optimal learning environment for our students or do we just accept the default?

I’m not saying that all defaults are bad. In cases where you’re not familiar with the field, you should probably accept the default settings. Computer security comes to mind. But, if you’re prepared to dig into the details a bit, then I’m sure you’ll find some settings that you’d rather change. Facebook privacy comes to mind. You don’t have to install open source software on your computers – although that would be a great start – and you don’t have to become an expert on everything you use. But you do need to know that every situation comes with a default configuration that someone else has set, and that you can change the settings.

The next time you are about to start something, ask if there are any changes you can make that will enhance the experience. Ask how much freedom you have to change the things you use. If you have no power to change the defaults then you’re accepting the choices that others have made about how you can teach. Just know that they didn’t make those choices based on what is best for students’ learning.


Teaching as improv performance

About a year ago I was introduced to the concept of using improv as a way of changing my thinking around teaching in the classroom, and the idea has been evolving at the back of my mind ever since. I thought it was time to get it out again.

I’m not a fan of improv theatre in the sense that I pay it a lot of attention but when I listen to interviews with people – usually actors – who got their start in improv, I’m usually quite impressed with how they connect with the person doing the interview. There’s something about improv training that seems to open you up to the possibility of connecting with others and playing off of their ideas. This seems like a useful starting point for thinking about teaching.


Another important part of improv is opening yourself up to making mistakes and showing a certain sense of vulnerability. It’s the same in the classroom; students need to know that it’s OK to make mistakes and that being wrong is not a point of failure. Often, the insight we get from being wrong can set the foundation for a powerful learning experience. But first you have to be OK with being wrong.

To be clear, teaching and using improv is not about being entertaining. You’re not trying to fill the time with jokes. Enjoyment of your time together is important but getting a laugh is not the same as delighting the audience. Improv is about creating a narrative and going on a journey together. And like the best journeys, there should be space for surprises, which means you must have less reliance on a script. The basic structure of the story is there but details are the things that you get to fill in together with your students.

Here are some basic principles of improv performance (there are many different lists, and none are rules):

  1. Listen to others
  2. Agree and support each other
  3. Respect your partner
  4. Believe in working together
  5. Don’t fear failure
  6. Bring a good energy
  7. Be comfortable being silly

The principles of improv performance may help us to introduce dynamic adaptation into our teaching practices in the classroom. The idea of roles, direction and narrative can be used to help us think differently about the actors in a classroom, as well as the relationship between those actors. We can use the connections we create to bring a story from the initial setup to a satisfying and potentially powerful conclusion.




Psychology’s top 20 principles for enhancing teaching and learning

Every once in a while an article is published that you know is Important and that you should take Note of, and in this post I’m going to summarise a paper that I think fits into that category. It’s a recent publication in Mind, Brain and Education that attempts to summarise and explain the Top 20 principles of teaching and learning, as determined by the last few decades of psychological research. The article is called Science Supports Education: The behavioural research base for Psychology’s top 20 principles for enhancing Teaching and Learning, and it’s by Lucariello, Nastasi, Anderman, Dwyer, Ormiston, and Skiba. See the bottom of this post for the abstract and citation information.

After a brief introduction and description of the Methods the article gets stuck into the principles, which I’ll list and describe below. For some reason, Principle 8 – on the development of student creativity – is not included in the paper and no explanation is given for the omission.

Principles 1-8: How do students learn?

1. Students’ beliefs or perceptions about intelligence and ability affect their cognitive functioning and learning: If students believe that intelligence has a fixed value, they are less likely to learn than if they believe that intelligence can be changed. Teachers should communicate to students that “…failure at a task is not due to lack of ability and that performance can be enhanced, particularly with added effort or through the use of different strategies.”

2. What students already know affects their learning: Students prior knowledge influences how they incorporate new ideas because what they already know interacts with the new material being learned. This is an especially important concept when considering students’ misconceptions and how those misconceptions impede new learning. Teachers could create tasks that give students an active role in confronting and then reducing their cognitive dissonance.

3. Students’ cognitive development and learning is not limited by general stages of development: Cognitive growth is uneven and not linked to stages. Therefore, teachers’ ideas around how, and what new material should be presented, are more effective when they can take into consideration the domain-relevant and contextual knowledge of their students.

4. Learning is based on context, so generalizing learning to new contexts is not spontaneous, but rather needs to be facilitated: In order for learning to be effective, it should generalise to new or different contexts and situations. However, student transfer of knowledge and skills is not spontaneous or automatic. Teachers could therefore teach concepts in multiple contexts so that students can recognise contextual similarities, and focus on the application of their knowledge to the real world.

5. Acquiring long-term knowledge and skill is largely dependent on practice: What people know is laid down in long-term memory and information must be processed before it can move from short-term to long-term memory. This processing is accomplished through different strategies, and practice is key. Teachers should consider a variety of frequent assessment tasks given at spaced intervals (distributive practice). In addition, interleaved practice (a schedule of repeated opportunities) to rehearse and transfer skills or content by practicing with tasks that are similar to the target task, or using several methods for the same task, is also recommended.

6. Clear, explanatory, and timely feedback to students is important for learning: Students should receive regular, specific, explanatory, and timely feedback on their work. Feedback is more effective when it includes specific information that is linked to current knowledge and performance to clear learning goals. Teachers should consider providing feedback on assessment tasks – particularly after incorrect responses – in order to improve classroom performance in the future.

7. Students’ self-regulation assists learning and self-regulatory skills can be taught: Self-regulatory skills include setting goals for learning; such as planning, and monitoring progress; and self-reflection, which consists of making judgements about performance and self-efficacy in reaching goals. Self-regulatory skills include the regulation of motivation, which consists of students’ knowledge, monitoring, and active management of their motivation or motivational processing. Teachers can teach these skills directly to learners, by modelling strategies or coaching on their effectiveness. Teachers can also provide opportunities for learners to set goals and manage their attainment and for self-appraisal. A reflective community also can be established by teachers.

8. Missing from this paper

Principles 9-12: What motivates students?

9. Students tend to enjoy learning and perform better when they are more intrinsically than extrinsically motivated: Learners who are intrinsically motivated engage in academic tasks for the pure enjoyment of such engagement, and are more likely to achieve at higher levels and to continue engaging with activities in the future. Intrinsic motivation is linked to effective learning because students persist longer at tasks, experience lower levels of anxiety and develop positive competence beliefs. Learners who are extrinsically motivated engage in tasks in order to receive a reward or avoid a punishment, and are at risk for a number of problematic long term outcomes. Teachers can facilitate intrinsic motivation by de-emphasising high-stakes assessment, by allowing students to engage in projects they are interested in, encouraging students to take academic risks and by ensuring that students have enough time to engage with tasks.

10: Students persist in the face of challenging tasks and process information more deeply when they adopt mastery goals rather than performance goals: When teachers emphasise test scores, ability differences, and competition, students are more likely to adopt performance goals. Moreover, when test scores and grades are presented publicly, students are encouraged to focus on performance goals. In contrast, when teachers emphasise effort, self-improvement, and taking on challenges, students are more likely to adopt mastery goals. At the same time, they are likely to use effective and more complex cognitive strategies, to persist at challenging tasks, to report being intrinsically motivated, and to report feeling efficacious. Mastery goals are therefore more likely to be adopted when grades and test scores are shared privately and not compared across individuals.

11. Teachers’ expectations about their students affect students’ opportunities to learn, their motivation, and their learning outcomes: In classroom settings, teachers’ expectations for students’ successes and failures influence student achievement and motivation. When educators hold high expectations for their students, they often rise to the occasion and achieve at high levels (provided that the necessary support structures are in place). In contrast, when teachers hold low expectations for student success, students may come to believe that they lack skills and abilities, and thus confirm the teachers’ expectations. It is important to understand that teachers may interact differently with students, and provide differential instruction, based on their expectations for each student’s success or failure, regardless of how accurate those expectations are.

12. Setting goals that are short term (proximal), specific, and moderately challenging enhances motivation more than establishing goals that are long term (distal), general, and overly challenging: Goal setting is the process by which an individual sets a standard of performance and is important for motivation because students with a goal and adequate self-efficacy are likely to engage in the activities that lead to achievement of that goal. Three properties of goal setting are important for motivation. First, short-term goals are more motivating than long-term goals because it is easier to assess progress toward short-term goals. Students tend to be less adept at thinking concretely with respect to the distant future. Second, specific goals are preferable to more general goals because it is easier to quantify and monitor specific goals. Third, moderately difficult goals are the most likely to motivate students because they will be perceived as challenging but also attainable.

Principles 13–15: Why are social context, interpersonal relationships, and emotional well-being important to student learning?

13. Learning is situated within multiple social contexts.

14. Interpersonal relationships and interpersonal communication are critical to both the teaching–learning process and the social–emotional development of students.

15. Emotional well-being influences educational performance, learning, and development.

These principles are interrelated and are represented in theory and research relevant to schools as systems that support psychological (social and emotional) well-being as well as cognitive development and academic learning. According to developmental–ecological theory, the child or learner is best viewed as embedded within multiple social contexts or ecosystems (e.g., school, family, neighbourhood, peer group), that influence learning:

  • Microsystem: student-teacher and student-student interactions influence learning
  • Ecosystem: microsystem interactions occur within a school where policies and norms (teaching and learning practices and organisational structure) influence learning
  • Macrosystem: ecosystems interact (e.g. school and families) within a society which reflects culture, values and norms

These interactions within and between systems influence students’ learning significantly, and are documented more extensively in the article (pg. 61-62).

Principles 16–17. How can the classroom best be managed?

16. Expectations for classroom conduct and social interaction are learned and can be taught using proven principles of behaviour and effective classroom instruction.

17. Effective classroom management is based on (1) setting and communicating high expectations, (2) consistently nurturing positive relationships, and (3) providing a high level of student support.

Classroom management is a fundamental, bedrock set of
procedures and skills that establish a climate for instruction and learning. Class and school rules must be positively stated, concrete, observable, posted, explicitly taught, frequently reviewed, and positively reinforced. This allows students to learn the social curriculum in each classroom and enables teachers to develop classroom climates that maximise student engagement and minimises conflict and disruption.

Classrooms that are structured to offer multiple opportunities for students to respond facilitate the development of quality teacher–student relationships, which in turn lead to fewer behavioural problems and increased academic performance. Students who are at risk for classroom disruption may need more attention to relationship-building in order to develop and maintain connections in the classroom.

Culturally responsive classroom management is an approach that aims to actively engage students by offering a curriculum that is relevant to their lives. Teachers demonstrate a willingness to learn about important aspects of their students’ lives and create a physical environment that is reflective of students’ cultural heritage. Culturally responsive teachers understand the ways in which schools reflect and perpetuate discriminatory practices of the larger society and are characterised as “warm demanders”; “strong yet compassionate, authoritative yet loving, firm yet respectful”.

Finally, a high ratio of positive statements / rewards to negative consequences, and nurturing an atmosphere of respect for all students and their heritage, builds trust in the classroom that can prevent behavioural conflict.

Principles 18–20: how to assess student progress?

18. Formative and summative assessments are both useful, but they require different approaches: Formative assessments are carried out during instruction and are aimed at improving learning in the classroom setting. Summative assessments measure learning at a given point in time, usually at the end of some period of instruction where they are used to provide a judgement about student learning. The goal of both formative and summative assessments is to produce valid, fair, useful, and reliable information for decision making. Teachers can also use their understanding of assessment information to decide whether they covered the material that they intended to cover, or to judge how effectively they met the objectives for student learning.

19. Students’ skill and knowledge should be assessed with processes that are grounded in psychological science and that have provided well-defined standards for quality and fairness: Valid and reliable assessments enable teachers to make inferences about what students are learning. To understand the validity of an assessment, there are four question that need to be considered:

  1. How much of what you intended to measure is actually being measured?
  2. How much of what you did not intend to measure actually ended up being measured?
  3. What consequences, either intended or unintended, occurred with the assessment?
  4. Do you have solid evidence to support your answers to the first three questions?

Validity is a judgement, over time and across a variety of situations, about what inferences can be drawn from the test data, and the consequences of using the test. Valid assessment entails specifying what an assessment is supposed to measure. Teachers can improve assessment quality by aligning teaching and testing. However, they should also:

  • Be mindful that valid tests in one context may not be valid for another
  • Ensure that high-stakes decisions be based on multiple measures, not on a single test
  • Examine outcomes for any discrepancies in performance among different cultural groups

20. Good use of assessment data depends on clear, appropriate, and fair interpretation: Effective teaching depends heavily on teachers being informed consumers of educational research, effective interpreters of data for classroom use, and good communicators to students and their families about assessment data and decisions that affect them. The interpretation of assessments involves addressing the following questions:

  1. What was the assessment intended to measure?
  2. On what are comparisons of the assessment data based? Are students being compared to one another? Or, are responses being directly compared to samples of acceptable and unacceptable responses?
  3. Are scores being classified using a standard or cut point, such as letter grades, or another indicator of satisfactory/unsatisfactory performance?How were these standards set?

Awareness of the strengths and limitations of any assessment is critical. Such awareness enables teachers to make others aware of important caveats, such as the imperfect reliability of scores and the importance of using multiple sources of evidence for high-stakes decisions.

And there you have it. Twenty principles (19 without the one on fostering student creativity) on how best to go about enhancing teaching and learning practices in the classroom. While I don’t think it’s feasible to try and incorporate all of these principles in every classroom session, it’s definitely worthwhile having these at the back of your mind when planning assessment tasks, assignments, lectures and activities in class. I also recommend reading the whole paper which provides additional insight and links to further reading that would be useful to dig into.


Psychological science has much to contribute to preK-12 education because substantial psychological research exists on the processes of learning, teaching, motivation, classroom management, social interaction, communication, and assessment. This article details the psychological science that led to the identification, by the American Psychological Association’s Coalition for Psychology in Schools and Education, of the “Top 20 Principles from Psychology for PreK-12 Teaching and Learning.” Also noted are the major implications for educational practice that follow from the principles.

Citation: Lucariello, J. M., Nastasi, B. K., Anderman, E. M., Dwyer, C., Ormiston, H., & Skiba, R. (2016). Science Supports Education: The behavioural research base for Psychology’s top 20 principles for enhancing Teaching and Learning. Mind, Brain and Education, 10(1), 55–67.

quotes Uncategorized

Quote: “Learning results from what the student does and thinks…”



Movies about teacher-student relationships

The untimely passing of Robin Williams a few weeks ago reminded me of an idea for a post that’s been on my mind for a while (apparently I’m not the only person who thought about this). I’ve always loved movies about teachers and students, and I wanted to share some of the ones that have stuck with me. The idea began as a list of movies that inspired me to teach but ended up as a list of movies about relationships between people, that just happened to have teachers and students as a common theme.


Dead poets society (1989)


Finding Forrester (2000)


Good Will Hunting (1997)


Pay it forward (2000)


Real genius (1985)


A beautiful mind (2001)


Remember the Titans (2000)


Radio (2003)


With honors (1994)


School ties (1992)


Coach Carter (2005)

curriculum learning physiotherapy teaching

Interview: The use of technology-mediated teaching and learning in physiotherapy education

Selection_001I was recently asked to do a short interview by Physiospot, on the use of technology-mediated teaching and learning in physiotherapy education. As it turns out, the bulk of the interview relates more specifically to a Scholarship of Teaching and Learning, rather than the use of technology. However, I think that this makes it potentially more relevant for physiotherapy educators, especially those who may not be interested in the “technology” aspect. Thanks to Rachael Lowe at Physiospot for the invitation to chat.

Here is the link to the interview.