Judgment accuracy of preservice teachers regarding student performance: The influence of attention allocation

August, 2020

Source: Teaching and Teacher Education. 2020, Vol. 94.

(Reviewed by the Portal Team)

In this study the authors pursue two research questions:
1. Do differences in attention allocation of preservice teachers to virtual students influence the accuracy of the judgment?
2. Is it possible that attention allocation moderates the effect of the social categories of students on judgment accuracy?
Specifically, this study addressed the following two hypotheses:
H1. The attention allocation of the judge significantly predicts the accuracy of the judgment.
Research on dual-process models shows that attribute-based processing results in higher judgment accuracy.
This attributed-based processing is accompanied by higher attention allocation to individuating information about a student.
Thus, the more attention a judge allocates to a student, the higher the accuracy of the judgment should be.
H2. The more a judge gains systematic information about a student, the less relevant the non-performance-related student characteristics are.
If judgments and their accuracy are biased by prominent student categories, this bias should be reduced by allocating more attention to attribute-based information of the students.
Thus, the authors assume that the effect of student categories on judgment accuracy is moderated by attention allocation.

In this online study, 168 preservice teachers (79.20% female) participated.
All of them were enrolled in a teacher training programme at two universities in Germany.
In the study sample, 84.0% of the participants had already successfully completed one school teaching internship as a mandatory part of their programme.
Participation in the study was voluntary.

The virtual classroom and the judgment task
The authors used a virtual classroom setting (Karst, Bonefeld, & Siebert, 2019).
Within this instrument, the participants saw 12 students’ names of a virtual second grade primary school class on the left side of their screen.
The names of these 12 students indicated the gender and migration background of the students. By clicking on a student’s name, further information about the student was displayed.
This further information consisted of two elements presented simultaneously.
(1) The participant saw the completed test booklet of the student in a standardized mathematics test involving multiplication problems.
(2) The participant saw the score achieved in this mathematics test signed by a fictive teacher and the maximum score to be achieved.
Participants could click on a student as often as they wanted.
The clicks were automatically saved in a log-file.
The participants’ judgment task was to estimate how the students would score in a forthcoming mathematics test involving multiplication problems.
The question they had to answer for each student was: “How many of these 11 multiplication problems will the student solve eight days later?”.
Thus, the authors requested the participants to make a judgment.
These judgments formed the data basis to calculate the judgment accuracy.

Findings and discussion
The study reveals significant results that support the theoretical linking of judgment accuracy and judgment processes and the moderating effect of attention allocation on the influence of student characteristics on teacher judgments.

Linking judgment process and accuracy
The results confirmed the expected relationship between the judgment process and the judgment accuracy of preservice teachers.
Higher attention allocation indicates more attribute-based processing, which leads to higher accuracy of judgment.
Therefore, if the participant does not finish the process with his or her first categorization, but processes additional information, then he or she starts with the re-confirmation process of this categorization.
In the study, the increase of click frequency on the students demonstrates this process of re-confirmation and indicates a higher attention allocation to individuating characteristics of the student.
For the teachers’ teaching action this implies, that rash and unsystematic inferences about students can be avoided by higher attention.
This higher level of attention to the readiness or learning status of students can be reached by asking well-thought questions more frequent, looking at worksheets more regularly or by implementing short formative assessments in the class.
As this evidence-orientation shows positive effects on students learning development (van der Scheer & Visscher, 2018), it should be fostered among teachers.
Considering the authors’ findings, they advise teachers (or preservice teachers) to adapt their focus on students depending on the desired instructional goal of the diagnostic situation during teaching.
If the teacher applies a diagnostic situation where he or she for example uses pre-planned questions to determine the prior knowledge of students, and the purpose is to gain knowledge about the achievement level of specific students to implement individualized instruction (remedial learning, student-specific guidance) then it is better to allocate attention specifically on these individual students within a class.
The judgments would then be expected to be more accurate for these individual students. If the same diagnostic situation (pre-planned questions) is aimed at implementing ability grouping within the classroom, then the teacher should consider the responses of the students equally (pay almost equal attention to the students of a class).
This allows the teacher to assess the ranking of the students more accurately and to assign them more accurately to groups for achievement-related internal differentiation.

Moderating effect of judgment process
For both student categories, gender and migration background, the moderating effects of attention allocation on judgment accuracy were obvious, although not directly comparable.
Additionally, the moderating effect differed depending on the chosen accuracy measure.
A direct and more specific focus on students with a migration background reduces the bias between migrant and non-migrant students but decreases judgment accuracy for migrant students.
Whereas, if students with a migration background are clicked on as much as non-migrant students, students with a migration background are judged more accurately (cf. Kaiser et al., 2017; Tobisch & Dresel, 2017).
With regard to the gender of the students, the results showed that a higher student-specific click frequency reduced the level error for female students, which was overall higher for female students than for male students. Whether this decrease in level error was due to over- or underestimation of female students remains unsolved.
The results do not provide such a clear picture as for the students with a migration background.
However, it can be assumed that no process of in-group favoritism was triggered in the case of female students, which would be obvious if the test results of female students were overestimated.
Finally, the preservice teachers judged the rank order of the female students more accurately than that of the male students, independently of the attention allocated to the individual students.
Accordingly, it can be concluded that a higher student-specific click frequency reduces the level error for female students and non-migrant students.
In this case, the increased attention has a moderating effect, indicating that more individuating information on the female or non-migrant students has been included in the judgment process.
Concerning the question of how to overcome teacher biases this implicates that with a more conscious and systematic information processing of students pre-requisites biases in (preservice) teacher judgments for specific student-groups will reduce (Fiske et al.,1999).
Additionally, this reduction of biases goes along with an increase in judgment accuracy for the female and non-migrant students.
This can be an indication that when dealing with stereotypes in teacher education, not only negative stereotypes, such as migrants are performing poorly, but also positive stereotypes, such as non-migrants are always performing well, need to be considered.

Fiske, S. T., Lin, M., & Neuberg, S. L. (1999). The continuum model: Ten years later. In S. Chaiken, & Y. Trope (Eds.), Dual-process theories in social psychology (pp. 231e254). New York: Guilford.
Kaiser, J., Südkamp, A., & Moller, J. (2017). The effects of student characteristics on € teachers’ judgment accuracy: Disentangling ethnicity, minority status, and achievement. Journal of Educational Psychology, 109(6), 871e888.
Karst, K., Bonefeld, M., & Siebert, J. (2019). Mannheimer Inventar zur Testung der Urteilsakkuratheitd MITU. [Mannheim inventory for testing judgement accuracy - MITU]. Retrieved from https://www.sowi.uni-mannheim.de/media/Lehrstuehle/sowi/Karst/Dateien/MI...
van der Scheer, E. A., & Visscher, A. J. (2018). Effects of a data-based decision-making intervention for teachers on students’ mathematical achievement. Journal of Teacher Education, 69(3), 307e320.
Tobisch, A., & Dresel, M. (2017). Negatively or positively biased? Dependencies of teachers’ judgments and expectations based on students’ ethnic and social backgrounds. Social Psychology of Education, 20(4), 731e752.

Updated: Sep. 30, 2020


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