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Background: Asking learners manually authored questions about their readings improves their text comprehension. Yet, not all reading materials comprise sufficiently many questions and many informal reading materials do not contain any. Therefore, automatic question generation has great potential in education as it may alleviate the lack of questions. However, currently, there is insufficient evidence on whether or not those automatically generated questions are beneficial for learners' understanding in reading comprehension scenarios. Objectives: We investigate the positive and negative effects of automatically generated short-answer questions on learning outcomes in a reading comprehension scenario. Methods: A learner-centric, in between-groups, quasi-experimental reading comprehension case study with 48 college students is conducted. We test two hypotheses concerning positive and negative effects on learning outcomes during the text comprehension of science texts and descriptively explore how the generated questions influenced learners. Results: The results show a positive effect of the generated questions on the participants learning outcomes. However, we cannot entirely exclude question-induced adverse side effects on learning of non-questioned information. Interestingly, questions identified as computer-generated by learners nevertheless seemed to benefit their understanding. Take Away: Automatic question generation positively impacts reading comprehension in the given scenario. In the reported case study, even questions recognized as computer-generated supported reading comprehension.

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OBJECTIVE: The goal of this article is to present and to evaluate a sensor-based functional performance monitoring system. The system consists of an array of Wii Balance Boards (WBB) and an exergame that estimates whether the player can maintain physical independence, comparing the results with the 30 s Chair-Stand Test (30CST). METHODS: Sixteen participants recruited at a nursing home performed the 30CST and then played the exergame described here as often as desired during a period of 2 weeks. For each session, features related to walking and standing on the WBBs while playing the exergame were collected. Different classifier algorithms were used to predict the result of the 30CST on a binary basis as able or unable to maintain physical independence. RESULTS: By using a Logistic Model Tree, we achieved a maximum accuracy of 91% when estimating whether player's 30CST scores were over or under a threshold of 12 points, our findings suggest that predicting age- and sex-adjusted cutoff scores is feasible. CONCLUSION: An array of WBBs seems to be a viable solution to estimate lower extremity strength and thereby functional performance in a non-invasive and continuous manner. This study provides proof of concept supporting the use of exergames to identify and monitor elderly subjects at risk of losing physical independence.

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Objective: The goal of this contribution is to develop a classifier able to determine if cybersickness (CS) has occurred after immersion in a virtual reality (VR) scenario, based on a combination of biosignals and game parameters. Methods: We collected electrocardiographic, electrooculographic, respiratory, and skin conductivity data from a total of 66 participants. In addition, we also captured relevant game parameters such as avatar linear and angular speed as well as acceleration, head movements, and on-screen collisions. The data were collected while the participants were in a 10-minute VR experience, which was developed in Unity. The experience forced rotation and lateral movements upon the participants to provoke CS. A baseline was captured during a first simple scenario. The data were then split in per-level, per-60-second, and per-30-second windows. Furthermore, participants filled a pre- and postimmersion simulator sickness questionnaire. Simulator sickness scores were then used as a reference for binary (CS vs. no CS) and ternary (no CS-mild CS-severe CS) classification patterns. Several classification methods (support vector machines, K-nearest neighbors, and neural networks) were tested. Results: A maximum classification accuracy of 82% was achieved for binary classification and 56% for ternary classification. Conclusion: Given the sample size and the variety of movement patterns presented in the demonstration, we conclude that a combination of biosignals and game parameters suffice to determine the occurrence of CS. However, substantial further research is required to improve binary classification accuracy to adequate values for real-life scenarios and to determine better approaches to classify its severity.

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