The Importance of Teachers’ Knowledge

Summary: A recent paper by Sadler et al. looked at what aspects of teachers’ knowledge were important in increasing students’ knowledge. They found that teachers’ subject knowledge was important (and argue that broad, not deep, knowledge is important) but the effect differed depending on whether the specific subject had a common associated misconception. In this case, subject knowledge wasn’t enough — teachers needed to know students’ likely misconceptions in order to have an effect on student knowledge.

Unfortunately, the paper is not available without a journal subscription, so I’m going to give a few more details here. The whole paper is centred on physics — whether it might apply to computing is an interesting question.

The Plan

The hypothesis was two-fold:

  1. Teachers’ knowledge of a physics concept affects gains in students’ knowledge of that concept.
  2. Teachers’ knowledge of common misconceptions of a concept also has an effect on gains in students’ knowledge.

The method was simple and cute. A series of multiple choice questions were developed to test certain concepts in physics (e.g. when a candle burns, where does the wax go?). The test was administered to the students before and after the year where the topic was covered — if they went from wrong to right, they were adjudged to have learnt the concept. But the test was also administered to the teachers, who were asked both to pick the right answer (assessing their own knowledge), and to pick what they thought was the incorrect answer most likely to be picked by students (assessing their awareness of likely student misconceptions).

When you burn a candle, where does the wax go? 59% of students wrongly said it becomes liquid. Original photo by Rafael Soto, CC BY-NC 2.0

As a bit of a complication, they also included a couple of maths and reading questions in the student test, and split their sample based on the performance in those questions. The idea was that the low maths/reading ability group includes those that didn’t bother on the test, and thus the split stops them muddying the results. (Another possible interpretation is that these students are sufficiently lacking in core skills that they’re unlikely to be able to make gains in science.)

Their sample was just under 10,000 students, and around 180 teachers. They put the results into a statistical model (hierarchical logistic regression) that I must admit I don’t know a whole lot about, so I’m not going to offer any comment/criticism on this method.

The Findings

The end result was a model of how the various factors (e.g. teachers’ performance on a question) affected the difference in students’ performance between the pre-test and post-test.

Strong Maths/Reading

Let’s start with the group who scored well on the maths/reading questions. In the case where a question had no common misconception, the model predicted that even if these students were faced with a teacher without the appropriate subject knowledge, they would still manage to learn. However, if the teacher did have the subject knowledge, the learning effect was doubled. That makes a clear argument for having teachers who have subject knowledge of revelant topics.

The medium/high classifications on the right are my own very simplistic way of giving the gist of the results: “very high” was an effect size of around 1 pre-test standard deviation. If you want to understand the full detail you will need to read the paper.

However, this teacher effect disappeared if the question had a common misconception. In this case, the teacher having the subject knowledge alone did not increase the student’s learning beyond the no-knowledge baseline learning. A learning effect was only seen if teachers had also demonstrated knowledge of the likely misconception in this area. The message here being that if the students have a common misconception (e.g. that the candle wax becomes liquid), the teachers need to know this in order to correct it and improve students’ learning.

Weak Maths/Reading

So in the strong maths/reading group, the predictions were borne out. Meanwhile, over in the weak maths/reading group, it’s a different story. Given no misconceptions and faced with a teacher without the relevant subject knowledge, they learnt nothing. If the teacher had the knowledge, the students did learn, but only a low amount — less than the strong group learnt without a knowledgeable teacher. In the case where there was a misconception, students learnt very little (or nothing) regardless of the teacher’s subject knowledge or knowledge of misconceptions. There are a variety of reasons that this group (which was only 23% of the whole) may have learnt less, and so interpreting the result too closely is difficult. See the paper for full discussion.


So in the strong group, the predictions were borne out: teacher knowledge mattered, but knowledge of likely student misconceptions was also needed. This effect persisted even after the authors introduced things like years of teaching experience as a factor. The authors also argue that subject knowledge is not really about deep knowledge of any given area, but instead an accurate knowledge across all areas/concepts being taught (even if shallow).

Thanks to Mark Guzdial for the original link to the paper. For another some comments by the authors on the study, see this article.

Relevance to Computing

Physics is a subject where students can clearly come to it with preconceptions about its concepts. Long before we teach students gravity, they will have dropped or thrown things. Other work also suggests that physics is about correcting wrong preconceptions. In computing, it’s less obvious that they will have preconceptions — but that doesn’t mean they won’t quickly form misconceptions. Part of Beth Simon et al’s SIGCSE work this year on peer instruction involved forming examples that would tease out problems in student knowledge via multiple choice questions so that they could be discussed among the students. It might be that using those sorts of questions in a study like this might show similar results for computing.

Edit: just after I posted this, Peter Newbury — who works with Beth Simon — posted his own summary of the paper over here which has a bit more detail than mine on the statistical results of the paper.

Reference: Sadler, Connert, Coyle, Cook-Smith and Miller, “The Influence of Teachers’ Knowledge on Student Learning in Middle School Physical Science Classrooms”, American Education Research Journal, early access, 2013.

14 thoughts on “The Importance of Teachers’ Knowledge

  1. Strange to choose the wax example. It is more a chemistry matter than a physics one. Would have expected to choose a more solidly physics issue.

    But very interesting outcome, especially the impact of teacher knowledge coupled with knowledge of student misconception.

    How does this square with Hattie’s work and his conclusion that teacher knowledge did not affect student outcome?

    1. The wax example was taken from the paper — the paper refers to physical sciences, you’re right that my use of “physics” is imprecise.

      The paper doesn’t cite Hattie, but their argument is that their item-level method is a very fine-grained assessment of teacher knowledge, in that the model looks at the teacher answer to the same question as the students, rather than a general coarse-grain level of subject knowledge (or proxy like teachers’ degrees). They say at the global test-level they find very small effects, but at the item-level they find a much stronger effect. Also, they say there is little cross-item effect; a teacher knowing about circuits (and/or misconceptions) matters little to the effective teaching of chemical reactions.

      1. Ok. Physical sciences makes a lot of sense. So the teacher who teaches physical science knows some misconceptions and has subject knowledge but not in all areas so the overall effect is similar to a teacher who does not know misconceptions?

  2. Coincidence, indeed, Neil, that we both post summaries of this paper on the same day. As if that wasn’t enough, I work with Beth Simon everyday at UCSD, both us struggling to glean why peer instruction with clickers works and how we can share that knowledge with instructors.

    1. Ah yes — presumably she’s probably thinking along similar lines about the links to her own work on multiple choice instructions within peer instruction?

      1. When Beth talks to instructors about using peer instruction, she always coaches them that by the end of the discussion, students need to know why the right answers are right and why the wrong are wrong. When one of those wrong answers is a common misconception – Bingo! It an instructor with both content and misconception knowledge to craft a good peer instruction question.

  3. Very nice post. Teachers knowledge is really very important for teaching specially for literature and Science classes. Teacher should be knowledge of almost every related topic or misconception. Sometime teacher have to follow concept of out of syllabus for better understanding. You have defined well about this by examples. Thanks for posting.

  4. Thanks for posting this review. I personally held on to many misconceptions even into to my degree. Those wonderful light-bulb moments when a good teacher or lecturer takes the time to bring clarity to the concepts. Misconceptions are frequently gained because students full the knowledge gaps with their own ideas. This helps them achieve a feeling of understanding in the short term. However these misconceptions are strongly held by students and they take some unpicking. I hadn’t really thought about training teachers to explicitly look for misconceptions, this research should be written into ITT programmes. So new teachers realise the gains that can be made by focusing on commonly held misconceptions and on ways to help students give up those ideas for more correct concepts.
    But, electricity – now that IS magic.

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