Intelligent Tutoring Systems (ITSs) for Math still use traditional data input methods: computers’ keyboard and mouse. However, students usually solve math tasks using paper and pen. Therefore, the gap between the manner the students work and the requirements imposed by these typing-based systems expose students to an extraneous cognitive load, impairing their learning. Our study investigates the impact of the data input method on students’ learning and fluency in solving equations using step-based math ITSs. More specifically, we have considered the standard typing and handwriting input methods. We hypothesized that the students would be more fluent using their handwriting with online recognition to solve math equations than using the typing input method. This fluency indicates a reduction in cognitive load, freeing working memory for logical reasoning instead of interface preconditions, leading to improved learning. We have conducted an experiment with 55 seventh-grade students from a private school to validate the hypothesis, randomly assigned to control and experimental groups. Each group used one of the input methods on two different devices (desktop computers and tablets). Although students using handwriting solved more equations and were faster than students who typed their equations, we could not find statistically significant differences in the learning between students that used typing or handwriting. Additionally, we have found that the input method used in a not ideal device (e.g., handwriting with a computer’s mouse instead of using a touch screen device) can negatively affect the students’ performance.
Emotions can influence cognitive development and are key elements to the teaching-learning process. Positive emotions (e.g., engagement) can improve the ability to solve problems, store information, and make decisions. On the other hand, negative emotions (e.g., boredom) reduce the capacity to process information at a deeper level, preventing learning to become effective. Therefore, students’ emotions must be regulated to hinder negative and to promote positive emotions during learning. To support the choice of the best intervention to regulate individual emotions, this article proposes an algorithm based on simulated data considering different individual performances in solving Algebra exercises. The results suggest that the proposed model has high success rates (over 90%) in the choice of interventions and may be applied in real scenarios.