Ya Ting Carolyn Yang

Comprehensive evaluation criteria for English learning websites using expert validity surveys

This study aimed to develop a set of evaluation criteria for English learning websites. These criteria can assist English teachers/web designers in designing effective websites for their English courses and can also guide English learners in screening for appropriate and reliable websites to use in increasing their English ability. To fulfill our objective, we employed a three-phase research procedure: (a) establishing a preliminary set of criteria from a thorough review of the literature, (b) evaluating and refining the preliminary criteria by conducting interviews with in-service teachers and learners, and (c) validating and finalizing the criteria according to expert validity surveys. The established criteria have 46 items, classified into 6 categories (the number of items within the category) - general information (12), integrated English learning (13), listening (4), speaking (6), reading (5), and writing (6). The general information evaluates the authority, accuracy, and format of the learning websites. The integrated English learning evaluates the overall information relevant to English learning materials as well as the common features of the four language skills. The criteria for listening, speaking, reading, and writing, for example, examine the suitable intonation, skills of discourse, classification of reading articles by their attributes, and the proper use of discussion boards for students when practicing their writing skills. Based on qualitative and quantitative analysis of the interviews and expert validity surveys, we confirmed the effectiveness of the developed evaluation criteria with satisfactory indexes of inter-rater reliability, content validity, and factorial validity.

Walking Pattern Classification and Walking Distance Estimation Algorithms Using Gait Phase Information

This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.

A Wearable Sensor Module With a Neural-Network-Based Activity Classification Algorithm for Daily Energy Expenditure Estimation

This paper presents a wearable module and neural-network-based activity classification algorithm for energy expenditure estimation. The purpose of our design is first to categorize physical activities with similar intensity levels, and then to construct energy expenditure regression (EER) models using neural networks in order to optimize the estimation performance. The classification of physical activities for EER model construction is based on the acceleration and ECG signal data collected by wearable sensor modules developed by our research lab. The proposed algorithm consists of procedures for data collection, data preprocessing, activity classification, feature selection, and construction of EER models using neural networks. In order to reduce the computational load and achieve satisfactory estimation performance, we employed sequential forward and backward search strategies for feature selection. Two representative neural networks, a radial basis function network (RBFN) and a generalized regression neural network (GRNN), were employed as EER models for performance comparisons. Our experimental results have successfully validated the effectiveness of our wearable sensor module and its neural-network-based activity classification algorithm for energy expenditure estimation. In addition, our results demonstrate the superior performance of GRNN as compared to RBFN.

A blended learning environment for individualized English listening and speaking integrating critical thinking

Critical thinking (CT) and English communication are recognized as two essential 21st century competencies. To equip students with these competencies and respond to the challenges of global competition, educational technology is being developed to enhance teaching and learning. This study examined the effectiveness of integrating CT into individualized English listening and speaking instruction using Moodle, a virtual learning environment. Individualized instruction was designed with three key elements, namely proficiency level grouping, individualized instructional strategies and materials, and individualized feedback. Participants were 83 students enrolled in a semester-long general education course at a large university in Taiwan. The four dependent measures were CT skills (CTS), CT dispositions (CTD), English listening, and speaking proficiency. Results from the one-group pretest-posttest design were evaluated by paired t-tests and a mixed design ANCOVA (analysis of covariance) in order to identify any statistically significant improvements following the intervention. The results of the study showed that learners participating in the treatment significantly improved in terms of English listening and speaking, as well as on all CTS subscales, with little change in CTD, apart from significant improvement on the subscale of open-mindedness. Limitations and suggestions for successful online CT-integrated instruction and implications for future research are provided.

A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition

Abstract This paper presents a k-nearest-neighbor classifier with HRV feature-based transformation algorithm for driving stress recognition. The proposed feature-based transformation algorithm consists of feature generation, feature selection, and feature dimension reduction. In order to generate significant features from ECG signals, two feature generation approaches: trend-based and parameter-based methods are proposed in this study. The trend-based method computes statistical features from long-term HRV variations, while the parameter-based method calculates features from five-minute HRV analysis. The kernel-based class separability (KBCS) is employed as the selection criterion for feature selection. To reduce computational load of the algorithm, principal component analysis (PCA) and linear discriminant analysis (LDA) are adopted for feature dimension reduction. Our experimental results show that the combination of KBCS, LDA, and PCA can achieve satisfactory recognition rates for the features generated by both trend-based and parameter-based methods. The main contribution of this study is that our proposed approach can use only ECG signals to effectively recognize driving stress conditions with very good recognition performance.

No improvement—reflections and suggestions on the use of Skype to enhance college students’ oral English proficiency

The article discusses an experiment performed to determine the effectiveness of conversations using the Skype Internet telephone service in improving the English language skills of Taiwanese college students. Students in the experimental group used Skype to practice English amongst themselves, while a comparison group attended university English classes. Results indicated the Skype users showed no improvement over the comparison group in terms of oral proficiency in English. The authors discuss several possible reasons for this outcome.

Virtual CEOs

The requirements of a contemporary workplace include the ability to think critically and creatively in order to solve problems and respond to changes in economic and social conditions. Unfortunately, vocational education often fails to prepare graduates for this environment due to limited resources, low student motivation, or the reliance upon outdated instructional strategies. The use of digital game-based learning (DGBL) for vocational education has been proposed, but has yet to be effectively implemented, particularly in terms of the promotion of higher order thinking skills (HOTS). Data from 68 eleventh grade vocational high school students were evaluated after a quasi-experimental, 27 week intervention. Pretest and posttest results were evaluated by MANCOVA and demonstrated that the experimental group (blended DGBL incorporating integrative HOTS activities) outperformed the comparison group (technology enhanced learning) in terms of creative thinking, critical thinking, problem solving, and academic achievement, with significant improvements on all four measures. While technology-enhanced learning was effective in promoting academic achievement and creative thinking, the DGBL condition was deemed most effective in providing an authentic context for developing employment-related skills and knowledge. Based on these results, a blended approach for DGBL, which incorporates instructor orchestration and scaffolding, provision of learning aids, and the use of collaborative learning, is recommended, particularly for vocational learners. This paper provides examples of a concrete model of DGBL instruction that was verified empirically as successful in significantly improving all three higher order thinking skills, including creative thinking, critical thinking, and problem solving. A blended approach to simulation games improved higher order thinking and learning.Creativity, critical thinking, problem solving all significantly improved.Teacher scaffolding, learning aids, and collaboration are key design elements.Digital games offer advantages over standard technology-enhanced learning.Vocational learners develop job-related knowledge and skills through simulations.

Technology-enhanced game-based team learning for improving intake of food groups and nutritional elements

Nutrition is a critical issue for educators, particularly given the unhealthy eating behaviors of many adolescents. While knowledge and self-reflection are important, learners must also be motivated and held accountable for their health behaviors. In order to foster healthy eating, a technology-enhanced approach, game-based team learning (GBTL), is proposed, based on social-interdependence theory. A cloud diet assessment system (CDAS) was designed for automatically providing feedback on the nutritional intake of learners through a meal analysis algorithm. Furthermore, a cloud server hosted a social competitive game which, in addition to in-class team learning activities, allowed teams to compete against each other on the basis of each groups dietary habits. A pre-test post-test quasi-experimental design evaluated the effectiveness of the GBTL group (E1) as compared to a group which received only metacognitive feedback from the CDAS (E2) and a comparison group (C). Female high school participants from three classes were randomly assigned to the three groups (C, n?=?31; E1, n?=?20; E2, n?=?37). Results demonstrate significant improvement for E2 in terms of most food groups (including Dairy, Meats and Protein, Vegetables, and Fruit), as well as for macronutrients, such as calories and dietary fiber, and micronutrients, including Calcium and Vitamin C and B2. Within- and between-group comparisons confirmed the advantage of the E2 group, suggesting that technology-supported GBTL can foster healthy eating habits among adolescents, improving most nutritional elements to nearly 100% the recommended daily intake. Female adolescents seldom receive sufficient intake of dairy, fruits, and vegetables.Social interdependence through technology was used to encourage healthy eating.A cloud-based approach utilized team experiments and a social competitive game.Nutritional intake significantly improved by food group, micro- and macronutrients.Within-group and between-group results support the effectiveness of the design.

An effective ECG arrhythmia classification algorithm

This paper presents an effective electrocardiogram (ECG) arrhythmia classification scheme consisting of a feature reduction method combining principal component analysis (PCA) with linear discriminant analysis (LDA), and a probabilistic neural network (PNN) classifier to discriminate eight different types of arrhythmia from ECG beats. Each ECG beat sample composed of 200 sampling points at a 360 Hz sampling rate around an R peak is extracted from ECG signals. The feature reduction method is employed to find important features from ECG beats, and to improve the classification accuracy of the classifier. With the features, the PNN is then trained to serve as classifier for discriminating eight different types of ECG beats. The average classification accuracy of the proposed scheme is 99.71%. Our experimental results have successfully validated the integration of the PNN classifier with the proposed feature reduction method can achieve satisfactory classification accuracy.

Using heart rate variability parameter-based feature transformation algorithm for driving stress recognition

This paper presents a heart rate variability (HRV) parameter-based feature transformation algorithm for driving stress recognition. The proposed parameter-based transformation algorithm consists of feature generation, feature selection, and feature dimension reduction. In order to generate significant features from ECG signals, parameter-based feature generation method is proposed in this study. The parameter-based method calculates features from five-minute HRV analysis. The kernel-based class separability (KBCS) is employed as the selection criterion for feature selection. To reduce computational load of the algorithm, principal component analysis (PCA) and linear discriminant analysis (LDA) are adopted for feature dimension reduction. Our experimental results show that the combination of KBCS, LDA, and PCA can achieve satisfactory recognition rates for the features generated by parameter-based feature generation method. The main contribution of this study is that our proposed approach can use only ECG signals to effectively recognize driving stress conditions with very good recognition performance.