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Mobile apps take advantage of the ubiquity of mobile phones and can be used to share unique pedagogical experiences with multimedia content not yet available in curriculums. This preliminary study used a quasi‐experimental pretest‐posttest design to examine changes in self‐reported empathy toward sexual orientation and gender identity (SOGI) minority people. We also report on the associations between gender and a physiological measure of emotional activation, skin conductance level (SCL), on self‐reported empathy. The main results of this study that examined 57 undergraduate students at a Canadian University whom interacted with a queer history app individually were the following: Preliminary evidence that (1) students' empathy toward SO and GI minorities can be measured using a modified version of the Scale of Ethnocultural Empathy (SEE). (2) Statistically significant increases in empathy toward SO and GI minorities pre to post app interaction. (3) Students' pre‐ and post‐empathy levels were statistically significantly higher toward SO than GI minorities. (4) Female students had statistically significantly higher self‐reported empathy toward SO and GI minorities than males. (5) Male students had statistically significantly higher SCL than females. (6) Statistically significant interaction between SCL grouping and questionnaire administration on GI minority empathy. Findings and implications are discussed in lieu of the contributions that mobile apps can play to support social change, in particular, by fostering empathy. [ABSTRACT FROM AUTHOR]

This paper presents the evaluation of the synchronization of three emotional measurement methods (automatic facial expression recognition, self-report, electrodermal activity) and their agreement regarding learners’ emotions. Data were collected from 67 undergraduates enrolled at a North American University whom learned about a complex science topic while interacting with MetaTutor, a multi-agent computerized learning environment. Videos of learners’ facial expressions captured with a webcam were analyzed using automatic facial recognition software (FaceReader 5.0). Learners’ physiological arousal was recorded using Affectiva’s Q-Sensor 2.0 electrodermal activity measurement bracelet. Learners’ self-reported their experience of 19 different emotional states on five different occasions during the learning session, which were used as markers to synchronize data from FaceReader and Q-Sensor. We found a high agreement between the facial and self-report data (75.6%), but low levels of agreement between them and the Q-Sensor data, suggesting that a tightly coupled relationship does not always exist between emotional response components. [ABSTRACT FROM AUTHOR]

• Little research examining multiple sources of emotion in achievement activities. • High levels of enjoyment, low levels of boredom across tech and topic emotions. • Task value predicted tech and topic enjoyment and boredom. • Tech control contributed to the model that predicted tech-related enjoyment. • Tech-related enjoyment and topic boredom predicted perceived success of learning. Little research has been conducted to differentiate between multiple, and frequently simultaneously available, discrete object foci in academic achievement situations that emotions can be generated from, including technology and academic topics. Using R. Pekrun's control-value theory of achievement emotions and Sharples and colleagues' Mobile Learning Theory, we examined whether appraisals of control over technology and task value predicted emotions directed toward using a mobile app (technology-related emotions) and queer history content (topic emotions). In turn, we examined whether technology-related and topic emotions predicted objective and subjective knowledge outcomes. The main results of this study that examined 57 undergraduate students at a Canadian University were the following: (1) Learners reported high mean levels of technological control over the app. (2) Relatively high mean levels of task value. (3) High mean levels of enjoyment and low mean levels of boredom across both technology-related and topic emotions. (4) Learners' appraisals of task value contributed to multiple regression models that statistically significantly predicted all emotions; appraisals of control over technology contributed to the multiple regression model (along with task value) that statistically significantly predicted technology-related enjoyment. And (5) technology-related enjoyment and topic boredom contributed to multiple regression models that statistically significantly predicted perceived success of learning. Findings and implications are discussed from a critical-analytical perspective. [ABSTRACT FROM AUTHOR]