Using refined kano model and decision trees to discover learners' needs for teaching videos.

With the advancement of technology and the spread of the COVID19 epidemic, learning can no longer only be done through face-to-face teaching. Numerous digital learning materials have appeared in large numbers, changing people's learning mode. In the era of information explosion, how to capture the learners' attention to teaching videos and improve learning effectiveness is the common goal of every designer of e-leaning teaching content. Previous researches focused on the analysis of learning effectiveness and satisfaction. Instructional designers only provided design elements with high learning effectiveness or high satisfaction, and lacked in-depth analysis of the learners' perspectives. The opinions of these e-learning users are often the key to the success of online teaching videos. Therefore, this study aims at the design elements that will be used in the teaching film. The operation mode of the piano mechanism will be employed as the content of the teaching film. Based on eight elements including arrow cueing, dynamic arrow cueing, spreading-color cueing, contrary to cueing, font style, color application, anthropomorphic, and audiovisual complementarity, we use Refined Kano Model to analyze learners' needs of categorization of each element, and discover learners' expectations for teaching videos. In addition, this study also conducts in-depth data analysis through decision trees algorithm, and stratification analyses using different variables (such as design expertise, using frequency, and usage experience, etc.) to find out the key design factors that affect learners' learning. Depending on the learner's background, the use of e-learning experience, using frequency, and the length of the learning video, our results could provide for reference when designing teaching videos. Instructional designers can better understand how to effectively use design elements, so that the teaching videos can achieve the best learning effect.

The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach.

The emergence of crowdfunding has given many capital demanders a new fund-raising channel, but the overall project success rate is very low. Many scholars have begun to discover key suscessful factors of crowdfunding projects. Previous studies have used questionnaires survey to identify important project features. In addition to requiring a lot of manpower and time, there may also be sampling bias. Moreover, related studies also reported that the project description will affect the success of the crowdfunding project, but there is no research to tell fundraisers which success factors should be included in the content of the project description. Besides, in recent years, game crowdfunding projects have been attracted lots of attention in terms of total fundraising amount and number of projects. Moreover, in traditional feature selection and text mining approaches, the selected terms are un-organized and hard to be explained. Therefore, this study will focus on real video and mobile game project descriptions to replace conventional questionnaires. To solve these issues, we present a lexicon-based feature selection method. We attempt to define "content features" and build lexicons to determine the attributes' values. Three feature selection methods including decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE) will be employed to find organized candidate key successful factors. Then, support vector machines (SVM) will be used to evaluate the performances of candidate feature subsets. Finally, this study has identified the key successful factors for video and mobile games, respectively. Based on the experimental results, we can give fundraisers some useful suggestions to improve the success rate of crowdfunding projects.

A novel weighted distance threshold method for handling medical missing values.

Data in the medical field often contain missing values and may result in biased research results. Therefore, the objective of this work is to propose a new imputation method, a novel weighted distance threshold method, to impute missing values. After several experiments, we find that the proposed imputation method has the following benefits. (1) The proposed method with purity can reassign instances into the nearest class of the dataset, and the purity computation can filter outliers; (2) The proposed method redefines the degree of missing values and can determine attributes and instances relative to the missing values in different datasets; and (3) The proposed method need not set the k value of the nearest neighborhood because this study identifies the k value based on the best threshold to calculate purity to enhance the results of imputation. In addition, the distance threshold can adjust the optimal nearest neighborhood to estimate missing values. This study implements several experiments to compare the proposed method with other imputation methods using different missing types, missing degrees, and types of datasets. The results indicate that the proposed imputation method is better than the listed methods. Moreover, this study uses the stroke dataset from the International Stroke Trial (IST) to verify whether the proposed method can be effectively applied in practice, and the results show that the proposed method achieves 90% accuracy in the Stroke dataset.

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media.