Use of Smartphone Apps, Social Media, and Web-Based Resources to Support Mental Health and Well-Being:Online Survey

Background: Technology can play an important role in supporting mental health. Many studies have explored the effectiveness, acceptability, or context of use of different types of mental health technologies. However, existing research has tended to investigate single types of technology at a time rather than exploring a wider ecosystem that people may use. This narrow focus can limit our understanding of how we could best design mental health technologies. Objective: The aim of this study was to investigate which technologies (smartphone apps, discussion forums and social media, and websites and Web-based programs) people use to support their mental health and why, whether they combine and use more than one technology, what purpose each technology serves, and which features people find the most valuable. Methods: We conducted an online survey to gather responses from members of the public who use technology to support their mental health and well-being. The survey was advertised on social media and via posters at a university. It explored usage patterns, frequently used features, and engagement with technology. To gain deeper insights into users’ preferences, we also thematically analyzed open-ended comments about each technology type and suggestions for improvements provided by the respondents. Results: In total, 81 eligible participants completed the survey. Smartphone apps were the most commonly used technology, with 78% of the participants (63/81) using them, either alone (40%) or in combination with other technologies (38%). Each type of technology was used for specific purposes: apps provided guided activities, relaxation, and enabled tracking; social media and discussion forums allowed participants to learn from the experiences of others and use that knowledge to understand their own situation; and Web-based programs and websites helped to find out how to deal on a day-to-day basis with stress and anxiety. The analysis of open-ended responses showed that although many people valued technology and felt it could support targeted activities, it was not seen as a substitute for traditional face-to-face therapy. Participants wanted technology to be more sophisticated and nuanced, supporting personalized and actionable recommendations. There was evidence that participants mistrusted technology, irrespective of the type, and had broader concerns regarding the impact of overuse of technology. Conclusions: People use different types of technology to support their mental health. Each can serve a specific purpose. Although apps are the most widely used technology, mixing and matching different types of technology is also common. Technology should not be seen as a replacement for traditional psychotherapy, rather it offers new opportunities to support mental health as part of an overall ecosystem. People want technology to be more nuanced and personalized to help them plan informed actions. Future interventions should explore the use of multiple technologies and their combined effects on mental health support

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Data Changes Everything - An Investigation Into The Acceptance Of Learning Analytics To Support Student Success

The development and implementation of learning analytics as a mechanism to support student success has been an emerging trend within Higher Education. Previous research identifies that learning analytics is an innovative educational development but recognises that little attention has been paid to evaluating its effectiveness or pedagogic usefulness. Researchers recognise that learning analytics is a new field in need of further research to aid its credibility within the educational arena. This research study provides a better understanding of learning analytics to support student success through the examination of opportunities and challenges of learning analytics from a multi-stakeholder perspective. This study also demonstrates how learning analytics can be successfully implemented within Higher Education. Through an interpretivist paradigm, this cross-sectional research study captures the unique experiences of students, academic staff and learning analytics experts. Data collected through twelve semi-structured interviews and three student focus groups enabled the researcher to gather a broad understanding of learning analytics from those involved and provides an holistic portrayal from this cultural group. The main findings of this study suggest that learning analytics need to have a clear context and purpose within Higher Education to ensure successful development, effectiveness and pedagogic usefulness. Effective organisational change, culture, academic and student engagement, ownership and motivation are paramount. Findings also indicate disparities in the implementation of learning analytics within Higher Education, which require resolution to ensure success, and there is some discussion about how challenges can be overcome to ensure effective institutional adoption and student success

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications

Quantified self-tracking, self-efficacy and emotional intelligence

The quantified self has emerged as a new framework for self-improvement using personal data analytics and multiple forms of self-tracking. This project has examined the relationships between self-tracking for both mood and time expenditures with both emotional intelligence and emotional regulatory self-efficacy in a mixed methods experimental design. Through 14 days of time and mood tracking, 12 participant’s emotional intelligence and emotional regulatory self-efficacy were examined in a pre- and post-test design; a significant (p < 0.01) relationship was observed for participants emotional regulatory self-efficacy in the domain of acting despite powerful emotions, while no other significant relationships were observed in this study. In multiple interviews, participants identified increased emotional understanding and reported some degree of behavioural change as a result. However, given the small sample size and multiple limitations, this study is only intended to serve as an exploratory framework for further research.self-trackingself-efficacyemotional intelligenceeffectivenesseducatio

A Quantitative Comparative Analysis of EdD Persistence Factors

Whether studying physical sciences, social sciences, engineering, mathematics, humanities, or education, approximately one in every two doctoral students fail to persist to degree completion (Bowen & Rudenstine, 1992; Lovitts, 2001; Tinto, 2012). A quantitative comparative study focused on two populations; students currently enrolled in the professional doctorate EdD program and former EdD students, including students who started but did not finish the program. Research-based variables, characterized as personal and program factors driving doctoral student attrition, were tested for significance. The participation criteria defined at least 80% of the program’s course content in totality was or is currently delivered online from a university offering the professional EdD degree, including affiliation with the Carnegie Project on the Education Doctorate (Allen & Seaman, 2015; Rockinson-Szapkiw et al., 2019). About half of the survey respondents attended an EdD program affiliated with the Carnegie Project on the Education Doctorate (CPED). In contrast, the other half attended an EdD program with no affiliation with CPED. The Community of Inquiry for Online Learning comprised four elements, teaching presence, social presence, cognitive presence, and emotional presence, and was the study’s theoretical framework. A total of [n = 725] individuals responded to surveys, which yielded a sample size of [n = 475] usable responses from former and current EdD students. The data from 30 former students, who did not persist, was analyzed for comparative purposes. Survey respondents represented a diverse population of age, gender, ethnicity, and marital status, attending public, private, and for-profit colleges and universities from geographic locations throughout the United States. The independent variable for all but the last of 16 hypothesis tests were current and former EdD students. The dependent variables were the personal and program factors. Five hypothesis tests included the effect of a moderating or second independent variable to reveal differences between the primary independent and dependent variables. The last hypothesis test compared time-to-degree between former students who attended an EdD program affiliated with the CPED and students who attended an EdD program with no affiliation with CPED. Within the 16 statements of hypothesis were 32 sub-hypotheses tests, of which the results indicated 19 were significant

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data