First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Student Perception Of General Education Program Courses

The purposes of this study were to: (a) determine, for General Education Program (GEP) courses, what individual items on the student form are predictive of the overall instructor rating value; (b) investigate the relationship of instructional mode, class size, GEP foundational area, and GEP theme with the overall instructor rating value; (c) examine what teacher/course qualities are related to a high (Excellent) overall evaluation or a low (Poor) overall evaluation value. The data set used for analysis contained sixteen student response scores (Q1-Q16), response number, class size, term, foundational area (communication, cultural/historical, mathematics, social, or science), GEP theme (yes/no), instructional mode (face-to-face or other), and percent responding (calculated value). All identifying information such as department, course, section, and instructor was removed from the analysis file. The final data set contained 23 variables, 8,065 course sections, and 294,692 student responses. All individual items on the student evaluation form were related to the overall evaluation item score, measured using Spearman\u27s correlation coefficients. None of the examined course variables were selected as significant when the individual form items were included in the modeling process. This indicated students employed a consistent approach to the evaluation process regardless of large or small classes, face-to-face or other instructional modes, foundational area, or percent responding differences. Data mining modeling techniques were used to understand the relationship of individual item responses and additional course information variables to the overall score. Items one to fifteen (Q1 to Q15), class size, instructional mode, foundational area, and GEP theme were the independent variables used to find splits to create homogenous groups in relation to the overall evaluation score. The model results are presented in terms of if-then rules for \u27Excellent\u27 or \u27Poor\u27 overall evaluation scores. The top three rules for \u27Excellent\u27 or \u27Poor\u27 based their classifications on some combination of the following items: communication of ideas and information; facilitation of learning; respect and concern for students; instructor\u27s overall organization of the course; instructor\u27s interest in your learning; instructor\u27s assessment of your progress in the course; and stimulation of interest in the course. Proportion of student responses conforming to the top three rules for \u27Excellent\u27 or \u27Poor\u27 overall evaluation ranged from 0.89 to .60. These findings suggest that students reward, with higher evaluation scores, instructors who they perceive as organized and strive to clearly communicate course content. These characteristics can be improved through mentoring or professional development workshops for instructors. Additionally, instructors of GEP courses need to be informed that students connect respect and concern and having an interest in student learning with the overall score they give the instructor

A study of security issues of mobile apps in the android platform using machine learning approaches

Mobile app poses both traditional and new potential threats to system security and user privacy. There are malicious apps that may do harm to the system, and there are mis-behaviors of apps, which are reasonable and legal when not abused, yet may lead to real threats otherwise. Moreover, due to the nature of mobile apps, a running app in mobile devices may be only part of the software, and the server side behavior is usually not covered by analysis. Therefore, direct analysis on the app itself may be incomplete and additional sources of information are needed. In this dissertation, we discuss how we can apply machine learning techniques in multiple tasks for security issues in regard of mobile apps in the Android platform. These include malicious apps detection and security risk estimation of apps. Both direct sources of information from the developer of apps and indirect sources of information from user comments are utilized in these tasks. We also propose comparison of these different sources in the task of security risk estimation to point out the necessity of usage of indirect sources in mobile app security tasks

China's Overt Economic Rise and Latent Human Capital Investment: Achieving Milestones and Competing for the Top

We provide an overview of China's economic rise through time. Over the past decade, China has maintained 10% growth in GDP, albeit with a GDP per capita at the low level of a developing country. Its tremendous economic development has overlooked the growing social inequalities and rising resentments of the ‘cheap’ workers and those laid off. The main contributor to its ascension is international trade and investment in physical capital, often at the expense of the environment. The year 1978 was the landmark for the foundation of the Chinese modern higher education system. Since then the number of students enrolled in Chinese higher education institutions has increased dramatically; China is producing serious scholars and a tremendous amount of scholarly output; more and more Chinese students seek higher education abroad; and international students find a rising interest in receiving education in China.China, human capital, brain drain, higher education

MOOCs as an Alternative for Teacher Professional Development. Examining Learner Persistence in One Chinese MOOC

Massive Open Online Courses (MOOCs) have developed into a significant international movement, showing great promise in addressing equity, quality, and efficiency issues in global education. To date, many MOOCs have been developed specifically for teacher professional development (TPD). In this regard, an important empirical question remains to be addressed: How and to what extent can MOOCs support equity, quality, and efficiency in teacher professional development? To help fill this knowledge gap, this study, conducted from 2014 to 2016, focused on persistent teacher-learners in a TPD MOOC that was offered for seven consecutive rounds by the X-Learning Center of Peking University. The study found that more than 15% of the 105,383 teachers who enrolled in this MOOC were persistent teacher-learners, defined as learners who enrolled in multiple rounds. Data analysis showed that these persistent Keywords: MOOC, teacher professional development, persistent teacher-learners, self-regulated learning teacher-learners had diverse motivations for re-enrollment, including refreshing conceptual understanding, achieving higher scores, earning course certification, and discussing practical problems. The study also found that the persistent teacher learners developed self-regulated learning skills in the course of multiple rounds of the MOOC and showed significantly higher learning achievement than one-time enrollees. Qualitative and quantitative analysis of both clicklog data and interview data revealed additional insights into the persistent teacherlearners’ learning within the MOOC and their real-world teaching practice beyond the MOOC. Overall, this study contributes to an improved understanding of the potential of MOOCs as an alternative TPD delivery mode in developing countries and sheds light on the future design of effective TPD through MOOCs.This work was created with financial support from the UK Government’s Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government’s Department for International Development; the International Development Research Centre, Canada or its Board of Governors; or the Foundation for Information Technology Education and Developmen

China's Overt Economic Rise and Latent Human Capital Investment: Achieving Milestones and Competing for the Top

We provide an overview of China's economic rise through time. Over the past decade, China has maintained 10% growth in GDP, albeit with a GDP per capita at the low level of a developing country. Its tremendous economic development has overlooked the growing social inequalities and rising resentments of the 'cheap' workers and those laid off. The main contributor to its ascension is international trade and investment in physical capital, often at the expense of the environment. The year 1978 was the landmark for the foundation of the Chinese modern higher education system. Since then the number of students enrolled in Chinese higher education institutions has increased dramatically; China is producing serious scholars and a tremendous amount of scholarly output; more and more Chinese students seek higher education abroad; and international students find a rising interest in receiving education in China.China, human capital, brain drain, higher education

Exploring the Link Between Students’ Usage of ALEKS and Their Performance on State Benchmark Assessments in Mathematics

The purpose of this mixed methods study was to investigate relationships between students’ ALEKS usage, teachers’ implementation of ALEKS, and student performance on the 2017 – 2018 LEAP 2025 mathematics assessment. The quantitative portion of the study involved district-level analyses and teacher-level analyses that explored relationships between students’ ALEKS usage and LEAP performance. The qualitative portion of the study took into consideration previous research findings that have reported associations between program implementation and student achievement. This portion of the study included thematic analyses that examined the following relationships: ALEKS implementation in relation to teacher groups (i.e., RtI 8, Math 8, Both, and Magnet), ALEKS implementation of each teacher and LEAP performance, and ALEKS implementation in relation to teacher rankings (i.e., high student achievement or HSA / low student achievement or LSA) and LEAP performance. Key findings from the quantitative analyses indicated that ALEKS usage in terms of time spent and concept mastery did not make a statistically significant impact on students’ LEAP performance for any of the teachers except one teacher. In contrast, ALEKS usage in terms of skill mastery made a statistically significant impact on students’ LEAP performance for HSA teachers and for one LSA teacher. However, low usage of ALEKS in terms of time spent limited my ability to fully assess the potential impact of ALEKS usage on students’ LEAP performance. Key findings from the qualitative analyses indicated that there were differences in ALEKS implementation amongst teacher groups. To control for group differences, this study focused on Math 8 teachers who used the ALEKS Middle School Math Course 3 curriculum; these teachers were ranked into student achievement groups HSA and LSA. In essence, ALEKS implementation of HSA teachers were more closely aligned with ALEKS (2017) Best Practices for program implementation compared to LSA teachers. ALEKS implementation of LSA teachers typically deviated from ALEKS (2017) Best Practices. Overall, these findings suggest that despite low usage of ALEKS in terms of time spent, teachers who more closely followed the recommendations of ALEKS (2017) Best Practices had positive statistically significant associations between students’ skill mastery on ALEKS and LEAP performance

Latent deep sequential learning of behavioural sequences

The growing use of asynchronous online education (MOOCs and e-courses) in recent years has resulted in increased economic and scientific productivity, which has worsened during the coronavirus epidemic. The widespread usage of OLEs has increased enrolment, including previously excluded students, resulting in a far higher dropout rate than in conventional classrooms. Dropouts are a significant problem, especially considering the rising proliferation of online courses, from individual MOOCs to whole academic programmes due to the pandemic. Increased efficiency in dropout prevention techniques is vital for institutions, students, and faculty members and must be prioritised. In response to the resurgence of interest in the student dropout prediction (SDP) issue, there has been a significant rise in contributions to the literature on this topic. An in-depth review of the current state of the art literature on SDP is provided, with a special emphasis on Machine Learning prediction approaches; however, this is not the only focus of the thesis. We propose a complete hierarchical categorisation of the current literature that correlates to the process of design decisions in the SDP, and we demonstrate how it may be implemented. In order to enable comparative analysis, we develop a formal notation for universally defining the multiple dropout models examined by scholars in the area, including online degrees and their attributes. We look at several other important factors that have received less attention in the literature, such as evaluation metrics, acquired data, and privacy concerns. We emphasise deep sequential machine learning approaches and are considered to be one of the most successful solutions available in this field of study. Most importantly, we present a novel technique - namely GRU-AE - for tackling the SDP problem using hidden spatial information and time-related data from student trajectories. Our method is capable of dealing with data imbalances and time-series sparsity challenges. The proposed technique outperforms current methods in various situations, including the complex scenario of full-length courses (such as online degrees). This situation was thought to be less common before the outbreak, but it is now deemed important. Finally, we extend our findings to different contexts with a similar characterisation (temporal sequences of behavioural labels). Specifically, we show that our technique can be used in real-world circumstances where the unbalanced nature of the data can be mitigated by using class balancement technique (i.e. ADASYN), e.g., survival prediction in critical care telehealth systems where balancement technique alleviates the problem of inter-activity reliance and sparsity, resulting in an overall improvement in performance