Ma. Mercedes T. Rodrigo

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We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students' cognitive-affective states during their use of three different computer-based learning environments. Students' cognitive-affective states are studied using different populations (Philippines, USA),(More)
We investigate the relationship between a student's affect and how he or she chooses to use a simulation problem-solving environment, using quantitative field observations. Within the environment studied, many students were observed gaming the system (cf. Baker et al, 2004), while few students engaged in off-task behavior. We analyze which affective states(More)
We compare the affect associated with an intelligent tutoring environment , Aplusix, and a simulations problem solving game, The Incredible Machine, to determine whether students experience significantly better affect in an educational game than in an ITS. We find that affect was, on the whole, better in Aplu-six than it was in The Incredible Machine.(More)
We study which observable affective states and behaviors relate to students' achievement within a CS1 programming course. To this end, we use a combination of human observation, midterm test scores, and logs of student interactions with the compiler within an Integrated Development Environment (IDE). We find that confusion, boredom and engagement in(More)
We attempt to automatically detect student frustration, at a coarse-grained level, using measures distilled from student behavior within a learning environment for introductory programming. We find that each student's average level of frustration across five lab exercises can be detected based on the number of pairs of consecutive compilations with the same(More)
— We study the affective states exhibited by students using an intelligent tutoring system for Scatterplots with and without an interactive software agent, Scooter the Tutor. Scooter the Tutor had been previously shown to lead to improved learning outcomes as compared to the same tutoring system without Scooter. We found that affective states and(More)
We analyze the antecedents of affective states in a simulation problem-solving environment, The Incredible Machine: Even More Contraptions, through quantitative field observations of high school students in the Philippines using that system. We investigate the transitions between affective states over time, finding that several affective states, including(More)
We investigate the relationship between students’ affect and their frequency of careless errors while using an Intelligent Tutoring System for middle school mathematics. A student is said to have committed a careless error when the student’s answer is wrong despite knowing the skill required to provide the correct answer. We operationalize the probability(More)
In recent years, machine-learning software packages have made it easier for educational data mining researchers to create real-time detectors of cognitive skill as well as of metacognitive and motivational behavior that can be used to improve student learning. However, there remain challenges to overcome for these methods to become available to the wider(More)
In recent years, there has been increasing interest in automatically assessing help seeking, the process of referring to resources outside of oneself to accomplish a task or solve a problem. Research in the United States has shown that specific help-seeking behaviors led to better learning within intelligent tutoring systems. However, intelligent tutors are(More)