A trial in which trainee teachers who were being taught to identify
pupils with potential learning difficulties had their work `marked' by artificial intelligence has found the approach significantly improved their reasoning.
Date:
April 11, 2022
Source:
University of Cambridge
Summary:
A trial which used artificial intelligence to train new teachers
to spot potential learning difficulties in pupils found that
the approach significantly improved their assessment skills. 178
trainees assessed six fictionalised pupils for potential signs
of conditions such as dyslexia and ADHD. Half received feedback
in the form of a pre-written 'expert solution', while the others
received feedback from AI. In a subsequent test of the quality of
their reasoning when predicting potential learning difficulties in
pupils, those who received the AI feedback scored 10 percentage
points higher than the other group. The researchers suggest that
AI may therefore be an effective substitute for close, personal
feedback on teacher training programmes when training these skills,
if one-to-one support is not available.
FULL STORY ==========================================================================
A trial in which trainee teachers who were being taught to identify
pupils with potential learning difficulties had their work 'marked' by artificial intelligence has found the approach significantly improved
their reasoning.
==========================================================================
The study, with 178 trainee teachers in Germany, was carried out by
a research team led by academics at the University of Cambridge and Ludwig-Maximilians- Universita"t Mu"nchen (LMU Munich). It provides some
of the first evidence that artificial intelligence (AI) could enhance
teachers' 'diagnostic reasoning': the ability to collect and assess
evidence about a pupil, and draw appropriate conclusions so they can be
given tailored support.
During the trial, trainees were asked to assess six fictionalised
'simulated' pupils with potential learning difficulties. They were
given examples of their schoolwork, as well as other information such as behaviour records and transcriptions of conversations with parents. They
then had to decide whether or not each pupil had learning difficulties
such as dyslexia or Attention Deficit Hyperactivity Disorder (ADHD),
and explain their reasoning.
Immediately after submitting their answers, half of the trainees
received a prototype 'expert solution', written in advance by a qualified professional, to compare with their own. This is typical of the practice material student teachers usually receive outside taught classes. The
others received AI- generated feedback, which highlighted the correct
parts of their solution and flagged aspects they might have improved.
After completing the six preparatory exercises, the trainees then took two similar follow-up tests -- this time without any feedback. The tests were scored by the researchers, who assessed both their 'diagnostic accuracy' (whether the trainees had correctly identified cases of dyslexia or ADHD),
and their diagnostic reasoning: how well they had used the available
evidence to make this judgement.
The average score for diagnostic reasoning among trainees who had received
AI feedback during the six preliminary exercises was an estimated 10
percentage points higher than those who had worked with the pre-written
expert solutions.
==========================================================================
The reason for this may be the 'adaptive' nature of the AI. Because
it analysed the trainee teachers' own work, rather than asking them to
compare it with an expert version, the researchers believe the feedback
was clearer. There is no evidence, therefore, that AI of this type would improve on one-to-one feedback from a human tutor or high-quality mentor,
but the researchers point out that such close support is not always
readily available to trainee teachers for repeat practice, especially
those on larger courses.
The study was part of a research project within the Cambridge LMU
Strategic Partnership. The AI was developed with support from a team at
the Technical University of Darmstadt.
Riikka Hofmann, Associate Professor at the Faculty of Education,
University of Cambridge, said: "Teachers play a critical role in
recognising the signs of disorders and learning difficulties in pupils and referring them to specialists. Unfortunately, many of them also feel that
they have not had sufficient opportunity to practise these skills. The
level of personalised guidance trainee teachers get on German courses
is different to the UK, but in both cases it is possible that AI could
provide an extra level of individualised feedback to help them develop
these essential competencies." Dr Michael Sailer, from LMU Munich, said: "Obviously we are not arguing that AI should replace teacher-educators:
new teachers still need expert guidance on how to recognise learning difficulties in the first place. It does seem, however, that AI-generated feedback helped these trainees to focus on what they really needed to
learn. Where personal feedback is not readily available, it could be
an effective substitute." The study used a natural language processing
system: an artificial neural network capable of analysing human language
and spotting certain phrases, ideas, hypotheses or evaluations in the
trainees' text.
==========================================================================
It was created using the responses of an earlier cohort of pre-service
teachers to a similar exercise. By segmenting and coding these responses,
the team 'trained' the system to recognise the presence or absence of
key points in the solutions provided by trainees during the trial. The
system then selected pre- written blocks of text to give the participants appropriate feedback.
In both the preparatory exercises and the follow-up tasks, the trial participants were either asked to work individually, or assigned to
randomly- selected pairs. Those who worked alone and received expert
solutions during the preparatory exercises scored, on average, 33%
for their diagnostic reasoning during the follow-up tasks. By contrast,
those who had received AI feedback scored 43%. Similarly, the average
score of trainees working in pairs was 35% if they had received the
expert solution, but 45% if they had received support from the AI.
Training with the AI appeared to have no major effect on their ability to diagnose the simulated pupils correctly. Instead, it seems to have made
a difference by helping teachers to cut through the various information
sources that they were being asked to read, and provide specific
evidence of potential learning difficulties. This is the main skill most teachers actually need in the classroom: the task of diagnosing pupils
falls to special education teachers, school psychologists, and medical professionals. Teachers need to be able to communicate and evidence their observations to specialists where they have concerns, to help students
access appropriate support.
How far AI could be used more widely to support teachers' reasoning skills remains an open question, but the research team hope to undertake further studies to explore the mechanisms that made it effective in this case,
and assess this wider potential.
Frank Fischer, Professor of Education and Educational Psychology at LMU
Munich, said: "In large training programmes, which are fairly common
in fields such as teacher training or medical education, using AI to
support simulation-based learning could have real value. Developing and implementing complex natural language-processing tools for this purpose
takes time and effort, but if it helps to improve the reasoning skills of future cohorts of professionals, it may well prove worth the investment."
========================================================================== Story Source: Materials provided by University_of_Cambridge. The original
text of this story is licensed under a Creative_Commons_License. Note:
Content may be edited for style and length.
========================================================================== Journal Reference:
1. Michael Sailer, Elisabeth Bauer, Riikka Hofmann, Jan Kiesewetter,
Julia
Glas, Iryna Gurevych, Frank Fischer. Adaptive feedback from
artificial neural networks facilitates pre-service teachers'
diagnostic reasoning in simulation-based learning. Learning and
Instruction, 2022; 101620 DOI: 10.1016/j.learninstruc.2022.101620 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/04/220411101341.htm
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