Multimedia hints are widely used in educational materials to support conceptual learning, yet their comparative effectiveness across modalities remains underexplored. Prior studies suggest that graphical hints can enhance learners' performance on physics problems, but it is unclear how they interact with other modalities such as text and voice. Understanding these interactions is essential for designing effective instructional tools. In this study, we investigated the effects of graphical, typographic, and vocal hints, individually and in combination, on students' problem-solving performance. A total of 162 students from a conceptual physics course participated in individual interviews and solved four sets of isomorphic problems. Each set included an initial problem (pretest), six training problems, a near transfer problem, and a far transfer problem. We employed a 2 × 2 × 2 between-subject quasi-experimental design to examine the effects of the three hint modalities. Results from paired-sample t-tests showed significant performance gains from pretest to both near and far transfer tasks, indicating that solving isomorphic problems with hints promotes learning. Among modalities, graphical hints led to better training performance than typographic or vocal hints. Notably, combining typographic and vocal hints produced worse outcomes than using either modality alone, contradicting the auditory superiority effect and suggesting potential cognitive overload. These findings highlight the effectiveness of visual support and caution against indiscriminate integration of multiple hint modalities. We provide evidence-based recommendations for designing multimedia instructional materials that optimize cognitive processing and support conceptual problem solving in physics.

Research has shown that students' responses to conceptual questions correlate with their eye movements.However, to what extent is it possible to predict whether a particular learner might answer a question correctly by monitoring their eye movements in real time?To answer this question, we used spatialtemporal eye-movement data from about 400 participants, as well as their responses to four conceptual physics questions with diagrams.Half of these data were used as a training set for a machine learning algorithm (MLA) that would predict the correctness of students' responses to these questions.The other half of the data were used as a test set to determine the performance of the MLA in terms of the accuracy of the prediction.We will discuss the results of our study with specific attention to the prediction accuracy of the MLA under different conditions.

Conceptual knowledge is only one part of what students need to learn to solve problems. Knowing how to reason through a problem is also an important skill. In this paper, we study the effects of a computer-based training on students' reasoning on introductory physics problems. Participants solved three sets of conceptual problems, each of them containing three training problems with solutions followed by one near transfer problem and one far transfer problem. We analyzed students' verbal answers to elucidate the reasoning resources that they activated to construct the different explanations to the problems. We found that the students changed their reasoning when presented with solutions to training problems and later posed a transfer problem. Our results provide insights into students' activation of their resources and the procedure they used to construct their reasoning in response to the training problems.

We investigated the effect of hint modality on students' eye movements on conceptual physics problems with diagrams. We recruited 57 students enrolled in a physics class for future elementary teachers. The participants were randomly assigned to conditions with no hints, visual hints, text hints, audio hints, and all possible hint modality combinations. We found that different hint modalities affect students' eye movements differently and the difference of students' eye movements relates to their problem-solving performance. The results of this study are different from the predictions based on Cognitive Theory of Multimedia Learning (CTML). Our results suggest that the cognitive process in physics problem solving may not be fully explained by CTML and therefore more research might be necessary in this area.