Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Are all ‘research fields’ equal? Rethinking practice for the use of data from crowd-sourcing market places

New technologies like large-scale social media sides (e.g., Facebook and Twitter) and crowdsourcing services (e.g., Amazon Mechanical Turk, Crowdflower, Clickworker) impact social science research and provide many new and interesting avenues for research. The use of these new technologies for research has not been without challenges and a recently published psychological study on Facebook led to a widespread discussion on the ethics of conducting large-scale experiments online. Surprisingly little has been said about the ethics of conducting research using commercial crowdsourcing market places. In this paper, I want to focus on the question of which ethical questions are raised by data collection with crowdsourcing tools. I briefly draw on implications of internet research more generally and then focus on the specific challenges that research with crowdsourcing tools faces. I identify fair-pay and related issues of respect for autonomy as well as problems with power dynamics between researcher and participant, which has implications for ‘withdrawal-withoutprejudice’, as the major ethical challenges with crowdsourced data. Further, I will to draw attention on how we can develop a ‘best practice’ for researchers using crowdsourcing tools

Emotional intelligence training intervention among trainee teachers: a quasi-experimental study

Background: Emotional intelligence (EI) has often been linked to improvements in professional performance. Indeed, generic competencies related to EI have been included in university curricula. However, learning EI involves significant time and effort on the part of students, and this may hinder the acquisition of specific content for each degree. In this study, an intervention to develop EI in higher education students is described and evaluated. Methods: The intervention consisted of eight group sessions performed in a regular course aiming to increase EI. The sessions included strategies and training on perceiving and understanding one’s own emotions and others’ emotions, identifying and understanding the impact one’s own feelings in adopting decisions, expressing one’s own emotions and the stress experienced, and managing both one’s own emotions and emotions of others. Participants were 192 students studying for a Master of Primary Education degree. A quasi-experimental nonequivalent control group pretest-posttest design was adopted. The effectiveness of the intervention was evaluated using multi-level analyses. Results: The results showed a significant improvement in the EI of students in the experimental group compared with the control group. Conclusions: This research demonstrates that it is possible to develop EI in higher education students, without hindering the acquisition of specific content competencies and, therefore, without interfering with their academic performance and without overburdening students with work outside the classroom. Trial registration: The experiment has been registered in the Initial Deposit of the Spanish Center for Sociological Research (CIS). 7/6/2015. http://www.cis.es/cis/opencms/ES/index.html.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant number EDU2015-64562-R

Smart-Home Concept for Remote Monitoring of Instrumental Activities of Daily Living (IADL) in Older Adults with Cognitive Impairment: A Proof of Concept and Feasibility Study

Assessment of instrumental activities of daily living (IADL) is essential for the diagnosis and staging of dementia. However, current IADL assessments are subjective and cannot be administered remotely. We proposed a smart-home design, called IADLSys, for remote monitoring of IADL. IADLSys consists of three major components: (1) wireless physical tags (pTAG) attached to objects of interest, (2) a pendant–sensor to monitor physical activities and detect interaction with pTAGs, and (3) an interactive tablet as a gateway to transfer data to a secured cloud. Four studies, including an exploratory clinical study with five older adults with clinically confirmed cognitive impairment, who used IADLSys for 24 h/7 days, were performed to confirm IADLSys feasibility, acceptability, adherence, and validity of detecting IADLs of interest and physical activity. Exploratory tests in two cases with severe and mild cognitive impairment, respectively, revealed that a case with severe cognitive impairment either overestimated or underestimated the frequency of performed IADLs, whereas self-reporting and objective IADL were comparable for the case with mild cognitive impairment. This feasibility and acceptability study may pave the way to implement the smart-home concept to remotely monitor IADL, which in turn may assist in providing personalized support to people with cognitive impairment, while tracking the decline in both physical and cognitive function