Economics Study Progress Alters Student Gaze Behavior During Knowledge Testing

A newly published eye-tracking study demonstrates that economics teacher education students exhibit distinct visual gaze patterns during multiple-choice testing depending on how academic advancement is measured. Conducted by educational researchers Sebastian Brückner and Olga Zlatkin-Troitschanskaia, the empirical investigation evaluated gaze behaviors across single-choice test items to understand how cognitive processing mediates study progress and subject knowledge. Through multilevel mediation analysis, the authors established that while overall semesters and completed macroeconomics courses both correlated positively with economics knowledge, their underlying cognitive pathways diverged. Specifically, completing specialized macroeconomics courses was associated with longer average fixation durations on the correct answer, or attractor, which partially mediated test performance. In contrast, advanced general semester standing was associated with reduced average fixation durations on incorrect choices, or distractors, indicating that broader academic tenure fosters rapid elimination of irrelevant information during task resolution rather than extended focus on correct options.

The investigation addresses a critical theoretical gap in higher education assessment, where positive correlations between general study progress and domain knowledge are frequently cited as proof of test validity without examining real-time cognitive processing. In university programs preparing future economics teachers, student progress can be assessed through broad discipline-specific indicators, such as semesters completed across a general business curriculum, or focused domain-specific indicators, such as completion of core macroeconomics modules. Prior research had shown positive associations between study progress indicators and test results, as well as connections between fixation durations on attractors or distractors and solution accuracy. However, earlier studies had not evaluated whether distinct dimensions of academic progress alter how examinees allocate visual attention across competing response options. By combining eye tracking with structural equation modeling, the researchers investigated whether domain specialization and general academic longevity engage separate problem-solving mechanisms.

The study evaluated 35 bachelor students of economics teacher education from one German university. The cohort comprised 16 female and 19 male students, representing 54 percent male participation, with a mean age of 23.14 years (standard deviation 2.62). Eleven percent spoke a foreign native language differing from the German test language. Approximately 26 percent possessed prior economics education from vocational or secondary school, and participants held an average university entrance grade of 2.62 (standard deviation 0.48). On a standardized economics academic self-concept scale, students averaged 97.8 points out of 154 (standard deviation 15.9). Participants had completed an average of 3.85 semesters (standard deviation 2.45), with two students omitting semester data. Curricular requirements prompted early subject coursework: five students had completed zero macroeconomics courses, 24 had completed one course, and six had completed two or more courses.

To assess economic knowledge, researchers administered the WiWiKom test, which adapts 25 tasks from the established US Test of Economic Literacy and the Test of Understanding in College Economics. Each item describes an economic scenario followed by a question and four response options, consisting of three incorrect distractors and one correct attractor. Correct responses were scored as one point and incorrect responses as zero points, producing a maximum possible test score of 25. The participants achieved an average score of 14.11 points with a standard deviation of 5.33. In total, the authors examined 875 task-solving events, representing 35 students solving 25 tasks each. Following the test, participants completed an online questionnaire measuring their semester progress, course completion, prior education, high school grade point average, native language, and academic self-concept, while receiving a course participation certificate to support testing motivation.

The eye-tracking setup adhered to twelve reporting standards, designated A1 to A12, ensuring methodological rigor. Gaze data were gathered using a stationary Tobii X3-120 infrared tracker operating at 120 Hertz, positioned beneath a 22-inch display monitor with 1920 by 1080 resolution and a 60 Hertz refresh rate. Pupil-corneal reflection tracked both eyes within Tobii Pro Lab software version 1.152.30002. Fixations were detected using an identification by velocity threshold filter at 30 degrees per second visual angle with a 60-millisecond minimum fixation duration, yielding a precision of 0.24 degrees of visual angle. Participants sat 60 to 70 centimeters from the monitor under supervised artificial lighting, moving freely in a defined virtual space without visual obstructions. Calibration employed nine dots and four validation coordinates, tolerating gaze errors up to 0.8 degrees visual angle while keeping blink-related data loss below five percent. Tasks appeared sequentially without time limits, and students answered using a reconfigured keyboard restricted to keys A through D, enabling one-handed entry without looking down.

To evaluate cognitive processing, the authors measured average fixation duration in seconds across five Areas of Interest: the task stem, attractor, and three distractors. Internal consistency across tasks was high, with a Cronbach’s alpha of 0.944 for average task fixation durations. Participants averaged a total fixation time of 6.77 minutes across the test (ranging from 2.85 to 11.12 minutes), yielding an average task processing time of 16.26 seconds (standard deviation 11.82). Gaze analyses revealed that mean fixation duration on the attractor was 2.14 seconds, slightly exceeding the 1.88 seconds recorded on distractors, with both fixation times showing a moderate positive correlation (r = 0.58, p < 0.01). Completed economics courses correlated positively with fixation times on attractors and distractors (r = 0.16, p < 0.001). Conversely, semester progress correlated negatively with fixation duration on distractors (r = -0.18, p < 0.001) and attractors (r = -0.08, p < 0.05).

Direct statistical analyses supported hypothesis one by confirming positive associations between study progress indicators and economics knowledge scores. Semester completion correlated significantly with aggregate test scores (r = 0.49, p < 0.01). Course participation displayed clear tiered differences: students who had taken zero macroeconomics courses solved 41 percent of items correctly, compared to 56 percent for those with one course, and 71 percent for those with two or more courses. Multilevel models using z-standardized predictors confirmed these patterns at the individual task level. In unadjusted logistic regressions, economics courses attended correlated with item scores (crude odds ratio 1.46, 95% confidence interval: 1.09, 1.96), as did semesters completed (crude odds ratio 1.54, 95% confidence interval: 1.15, 2.05). After conditioning on potential confounders, significant relationships persisted for macroeconomics courses (adjusted odds ratio 1.32, 95% confidence interval: 1.04, 1.67) and semesters (adjusted odds ratio 1.43, 95% confidence interval: 1.15, 1.78).

To test mediation mechanisms, the researchers calculated two multilevel 2-1-1 models using generalized structural equation modeling following Krull and MacKinnon. In the first model, macroeconomics courses correlated significantly with item scores (beta = 0.354, p = 0.002) and with total fixation duration on the attractor (beta = 0.343, p = 0.025), whereas distractor fixations showed no significant relationship. Accounting for attractor fixation reduced the direct course effect, demonstrating a partial mediation where 30.00 percent of the total effect operated indirectly. In the second model, semesters completed correlated significantly with item scores (beta = 0.276, p = 0.008) and negatively with distractor fixation duration (beta = -0.289, p = 0.041), without linking significantly to attractor fixations. Incorporating distractor fixations reduced the direct semester coefficient from beta = 0.431 (p = 0.003) to beta = 0.276, with 41.67 percent of the total effect operating indirectly. Hypothesis two was thus confirmed with limitations: partial mediation appeared in both models, but operated through opposing response options.

These divergent mediation patterns reflect established cognitive frameworks. Under Alexander and colleagues’ Model of Domain Learning, course selection reflects not merely subject knowledge but specific student interest and inclination. Students possessing deeper domain insight focused more heavily on relevant content, exhibiting an attractor-preference strategy. Conversely, semester standing captures broad progression across general business fields like marketing and accounting. In accordance with the cognitive theory of visual expertise and Haider and Frensch’s information-reduction hypothesis, senior students process test material more rapidly, spending less time on irrelevant distractors and eliminating them during task solving. Control variables matched expectations from previous literature: male gender favored higher test scores, foreign native language presented a disadvantage, and higher entrance grades and academic self-concept predicted higher performance.

The authors noted several methodological limitations and future research opportunities arising from their exploratory investigation. Because the study analyzed a sample of 35 teacher education students from a single German university, replication studies across larger cohorts, additional institutions, and related domains are necessary to establish external validity. Furthermore, eye-tracking metrics alone cannot provide an exhaustive description of task-solving strategies spanning guessing, elimination, and deduction. Future research could incorporate think-aloud protocols, cognitive interviews, participant surveys on response confidence, and metrics such as transitive gaze behavior and recursive saccades to better explain decision processes. In addition, manipulating distractor characteristics, weighting response choices, and testing alternative formats like open-ended questions could reveal further strategy variations. Expanding prior knowledge measures to include vocational training or secondary coursework could also clarify specific comprehension difficulties, assisting educators in designing targeted didactic interventions for economics teacher training programs.

Subject of Research: Eye tracking and visual gaze behavior during economics knowledge testing

Article Title: Effects of teacher students’ study progress on their gaze behavior while solving of an economics knowledge test

Article References: Brückner, S., & Zlatkin-Troitschanskaia, O. (2024). Effects of teacher students’ study progress on their gaze behavior while solving of an economics knowledge test. Empirical Research in Vocational Education and Training, 16(1), Article 18. https://doi.org/10.1186/s40461-024-00172-2

Image Credits: AI Generated

DOI: 10.1186/s40461-024-00172-2

Keywords: eye tracking, economics education, teacher students, WiWiKom test, gaze behavior, fixation duration, multilevel mediation analysis, study progress, macroeconomics courses, attractor preference, distractor elimination, academic self-concept, single-choice tasks, Tobii eye tracker, cognitive processing, visual attention, higher education assessment