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BackgroundAviophobia (the fear of flying) can greatly impact the daily life functioning of people with the condition. Traditional exposure-based treatment is hampered by the limited availability of airplane practice situations, which is a result of economical and practical concerns. Easily accessible and low-cost virtual reality exposure therapy may address these challenges. ObjectiveThe purpose of our study is to investigate the effectiveness of ZeroPhobia: Aviophobia (a self-help mobile app–based treatment) in reducing flight anxiety symptoms and depressive and anxiety symptoms. We will also investigate the effects of usage intensity, the sense of immersion, inherent absorption ability, and perceived user-friendliness on the treatment effect. MethodsParticipants (N=114) who are aged 18-64 years and experience at least mild symptoms of aviophobia will be recruited from the general Dutch population and randomized into a treatment group or waitlist control group. By using their own phones and rudimentary mobile virtual reality headsets, participants will receive six modules of psychoeducation and cognitive behavioral therapy, which will include six levels of virtual reality exposure therapy over a period of 6 weeks. Assessments will be conducted at baseline, posttest (ie, after 6 weeks), and 3- and 12-month follow-ups. The primary outcome measure of our study is the Flight Anxiety Situations Questionnaire. The secondary outcome measures include anxiety and depression measures and additional covariates (including usage intensity, the degree of immersion, etc). We will test treatment effectiveness by conducting an intention-to-treat analysis and estimating average treatment effects on the treated. The mechanisms of treatment effect will also be explored. ResultsThe study was funded on September 25, 2018. Ethical approval was received on October 11, 2019. Recruitment closed on May 7, 2020. ConclusionsOur study will further the scientific understanding and clinical implications of technology’s current ability to aid in providing effective, accessible treatment for the fear of flying. Trial RegistrationNetherlands Trial Registry NL70238.029.19; https://www.trialregister.nl/trial/8257. International Registered Report Identifier (IRRID)DERR1-10.2196/22008

BackgroundThe lack of availability of evidence-based services for people exposed to adversity globally has led to the development of psychological interventions with features that will likely make them more scalable. The evidence for the efficacy of e-mental health from high-income countries is compelling, and the use of these interventions could be a way to increase the coverage of evidence-based psychological interventions in low- and middle-income countries. Step-by-Step is a brief (5-session) intervention proposed by the World Health Organization as an innovative approach to reducing the suffering and disability associated with depression. ObjectiveThis study aims to evaluate the effectiveness and cost-effectiveness of a locally adapted version of Step-by-Step with Syrian nationals (trial 1) and Lebanese nationals and other populations residing in Lebanon (trial 2). MethodsThis Step-by-Step trial involves 2 parallel, two-armed, randomized controlled trials comparing the e-intervention Step-by-Step to enhanced care as usual in participants with depressive symptoms and impaired functioning. The randomized controlled trials are designed and powered to detect effectiveness in 2 populations: Syrians in Lebanon (n=568) and other people residing in Lebanon (n=568; Lebanese nationals and other populations resident in Lebanon). The primary outcomes are depressive symptomatology (measured with the Patient Health Questionnaire-9) and functioning (measured with the World Health Organization Disability Assessment Scale 2.0). Secondary outcomes include anxiety symptoms, posttraumatic stress disorder symptoms, personalized measures of psychosocial problems, subjective well-being, and economic effectiveness. Participants are mainly recruited through online advertising. Additional outreach methods will be used if required, for example through dissemination of information through partner agencies and organizations. They can access the intervention on a computer, tablet, and mobile phone through a hybrid app. Step-by-Step has 5 sessions, and users are guided by trained nonspecialist “e-helpers” providing phone-based or message-based support for around 15 minutes a week. ResultsThe trials were funded in 2018. The study protocol was last verified June 20, 2019 (WHO ERC.0002797) and registered with ClinicalTrials.gov (NCT03720769). The trials started recruitment as of December 9, 2019, and all data collection was completed in December 2020. ConclusionsThe Step-by-Step trials will provide evidence about the effectiveness of an e-mental health intervention in Lebanon. If the intervention proves to be effective, this will inform future scale-up of this and similar interventions in Lebanon and in other settings across the world. Trial RegistrationClinicalTrials.gov NCT03720769; https://clinicaltrials.gov/ct2/show/NCT03720769 International Registered Report Identifier (IRRID)DERR1-10.2196/21585

Participation in educational activities is an important prerequisite for academic success, yet often proves to be particularly challenging in digital settings. Therefore, this study set out to increase participation in an online proctored formative statistics exam by digital nudging. We exploited targeted nudges based on the Fogg Behaviour Model, highlighting the relevance of acknowledging differences in motivation and ability in allocating nudges to elicit target behaviour. First, we assessed whether pre-existing levels of motivation and perceived ability to participate are effective in identifying different propensities of responsiveness to plain untailored nudges. Next, we evaluated whether tailoring nudges to students' motivation and perceived ability levels increases target behaviour by means of a randomized field experiment in which 579 first-year university students received 6 consecutive emails over the course of three weeks to nudge behaviour regarding successful participation in the online exam. First, the results point out that motivation explains differences in engagement as indicated by student responsiveness and participation, whereas the perceived ability to participate does not. Second, the results from the randomized field experiment indicate that tailored nudging did not improve observed engagement. Implications for the potential of providing motivational information to improve participation in online educational activities are discussed, as are alternatives for capturing perceived ability more effectively.

Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for dropout prevention explicit. Data of a Dutch vocational education institute is used to show how out-of-sample machine learning predictions can be used to formulate invitation rules in a way that targets students at risk more effectively, thereby facilitating early detection for effective dropout prevention.

In educational contexts where many domains subject to improvement are interdependent and causal evidence is frequently lacking it is difficult, if not impossible, for policymakers and educational practitioners to decide which domain should be invested in. This paper proposes a new method that uses Conditional Mean Independent Correlations (CMIC) and normative growth functions to inform such decision-making processes. In this paper, CMIC and growth functions are applied to data from a research-practice partnership to identify high impact improvements among domains that are considered important to the district's mission and vision around student learning. The results point to improvement domains that administrators did not consider to be high impact improvements initially, suggesting that this method brings leaders food for thought around strategies for improvement efforts. The CMIC and growth functions moreover accommodate opportunities for policymakers and practitioners to base their decisions on theory and data, providing them with a stronger degree of decision-making authority for use of resources for improvement. Simultaneously, CMIC and growth functions enable researchers to test and further develop theoretical models on improvement efforts. Limitations and suggestions for further research are discussed.

For decades, self-report instruments -- which rely heavily on students' perceptions and beliefs -- have been the dominant way of measuring motivation and strategy use. Event-based measures based on online trace data arguably has the potential to remove analytical restrictions of self-report measures. The purpose of this study is therefore to triangulate constructs suggested in theory and measured using self-reported data with revealed online traces of learning behaviour. The results show that online trace data of learning behaviour are complementary to self-reports, as they explained a unique proportion of variance in student academic performance. The results also reveal that self-reports explain more variance in online learning behaviour of prior weeks than variance in learning behaviour in succeeding weeks. Student motivation is, however, to a lesser extent captured with online trace data, likely because of its covert nature. In that respect, it is of importance to recognize the crucial role of self-reports in capturing student learning holistically. This manuscript is 'frontline' in the sense that event-based measurement methodologies with online trace data are relatively unexplored. The comparison with self-report data made in this manuscript sheds new light on the added values of innovative and traditional methods of measuring motivation and strategy use.

Early Warning Systems (EWS) in higher education accommodate student counsellors by identifying at-risk students and allow them to intervene in a timely manner to prevent student dropout. This study evaluates an EWS that shares student-specific risk information with student counsellors, which was implemented at a large Dutch university. A randomised field experiment was conducted to estimate the effect of EWS-assisted counselling on first-year student dropout and academic performance. The results show that the EWS accurately predicts at-risk students. Yet, EWS-assisted counselling did not reduce dropout, nor improved academic performance. Solving the underlying problem of poor academic performance might require additional actionable feedback and recommended counselling practices.

Virtual proctoring technology is credited with increasing the accessibility of exams by enabling students to participate in exams at any time and place. In this field experiment, students were randomly assigned to virtual proctoring or traditional on-campus examination to evaluate the effect, nature and timing of implementation barriers for online proctoring in higher education. The results show that the uptake in the traditional examination setting in this study was six times higher due to technical hurdles that students experienced with the virtual proctoring software. When alleviating these implementation barriers by offering all students the chance to participate in an unproctored retake, students initially assigned to virtual proctoring still showed a significantly lower uptake rate. Uptake could not be related to student characteristics, such as performance levels and motivation.

The quality assurance and evaluation of primary schools requires early risk detection. This is a daunting task, not only because risks are typically rare and their origins complex, but also because governing institutions have limited resources and capacity and desire efficiency and proportionality. Many countries, including most Organisation for Economic Co-operation and Development countries, use inspections to detect schools at risk. In order to aid these efforts, we first evaluate various case-based prediction models, and then propose a principled exploit-explore procedure for organizing school inspections. We demonstrate these methods using data from the Dutch Inspectorate of Education, which monitors the regulatory compliance of roughly 6,000 primary schools. The approach has the potential to balance the benefits of prioritizing inspections of predicted high-risk schools on the one hand, with the benefits of verifying predicted risks and causal impact evaluations of school inspections on the other hand.

Young people may adapt to vision impairment (VI) in a variety of ways. To explore heterogeneity in social participation, data were mined for distinct patterns. Existing data from 258 young people with VI, aged between 26 and 44 years (M = 35.3 years; SD = 5.07), were clustered. Social participation was measured by four closed-ended questions and the Social Network Map. Loneliness was measured by the De Jong Gierveld Loneliness scale. K-medoids clustering with the Partitioning Around Medoids (PAM) algorithm was used to cluster social participation variables and age.Eight clusters were identified, with participants in the two largest clusters, clusters 1 (n = 84) and 2 (n = 50), scoring high on almost all social participation outcomes. Participants in cluster 8 (n = 14) had the lowest social participation scores. However, also over half of the participants in clusters 4, 5, 6, and 7 experienced feelings of loneliness. Findings demonstrate considerable heterogeneity in social participation among young people with VI, which however does not link to loneliness. These variations in social participation underline the need for a differentiated approach to address needs of young people with VI. [ABSTRACT FROM AUTHOR]

Academic dismissal policies are increasingly implemented to promote academic success, with existing empirical evidence mostly restricted to short-run outcomes. This study examines long-term academic outcomes of academic dismissal for two cohorts (N=1707) of first-year bachelor students in Economics and Business at a Dutch university. Using administrative records, regression discontinuity design estimates suggest that academic dismissal does not relate to a difference in the propensity of graduation, nor to a change in study delay, when comparing students around the academic dismissal threshold. Not meeting this credit-threshold forces students to leave, and most decide to re-enroll in the same (43.4%) program elsewhere or at least within the academic domain (41.9%). Thus, while academic dismissal forces students to switch, its intended purpose of redirecting students to a different field of study is not observed. Implications for why academic dismissal might not deliver on the intended efficiency or effectiveness gains are discussed.

Author(s): Pim Cuijpers, Eva Heim, Jinane Abi Ramia, Sebastian Burchert, Kenneth Carswell, Ilja Cornelisz, Christine Knaevelsrud, Philip Noun, Chris van Klaveren, Edith van't Hof, Edwina Zoghbi, Mark van Ommeren, Rabih [...]

This study examines whether providing students with information on their future study success will influence their higher education enrollment decision and lower first-year dropout as a consequence. A randomized field experiment is conducted among 313 law and social science applicants at the Vrije Universiteit Amsterdam. The main results suggest that (a) students are generally overly positive about their future performance, (b) enrollment rates increase by 25% if students receive information on future study success, but (c) providing information to students does not reduce first-year dropout. An important conclusion is that the higher enrollment decision is not driven by the extent to which students are self-serving biased or by their updating behavior. Instead this decision seems to be influenced by a fear of failure, in the sense that students who receive a pass-signal (fail-signal) with respect to future study success enroll with higher (lower) probability.

A prerequisite for low-stakes activities to improve learning is to keep students engaged when confronted with challenging material. Comparing a personalized and non-personalized version of computerized practising, this study experimentally evaluates the relationships between student effort and ability across different dimensions of task perceptions. Students practise longer when the task is perceived to be not too difficult. Students assigned to a personalized version of the tool have a lower success rate while practising, but this does not translate to differences in practice intensity, task perceptions, or summative test scores. In a personalized practising environment, perceived interest and usefulness both have the potential to promote engagement, albeit in different ways. Students with a lower level of subject-specific ability find the tool more interesting and, particularly, consider the non-personalized tool difficult but useful. Instead, in the personalized condition, it is the group of students with higher prescores who value the tool as relatively useful. These results indicate that more students will remain engaged with adaptive practising if software takes into account such differences. Multiple approaches and algorithms may thus be necessary to optimally adapt practising to individual learners.

This article empirically examines the implications of competition among Dutch secondary schools: (1) regarding the sorting of students by performance levels in schools at the beginning of secondary education; and (2) regarding performance gains in the secondary school career, controlling for the aforementioned sorting patterns. We used data from about 13,000 students enrolled at 102 school locations in The Netherlands. Using differences in the distribution of competition intensity across local education markets, we applied Kernel estimation techniques to match students from relatively high- to low-competitive markets on the basis of student and household characteristics. Our results indicate that, with increasing competition, relatively more schools target the group of high-achieving students. As a result, schools will arguably have to enrol more "students at the margin" to ensure sufficient enrolment rates. To conclude, we observed that, accounting for sorting patterns, competition was related to small negligible improvements in academic achievement at the bottom of the distribution of student performance within the first three years of secondary education. Furthermore, a negative result for competition was found for categorical academic classrooms settings.

Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students' dropout based on an integrated comparative study of two universities located in two different European countries: Politecnico di Milano (Italy) and Vrije Universiteit Amsterdam (the Netherlands). This research aims at assessing whether the factors affecting dropout are similar in the Italian and the Dutch contexts by testing the predictive capacity of ad-hoc models trained in other university-country settings at three different stages of the student's university journey: (i) enrolment, (ii) end of the first semester, and (iii) end of the first year. Results show that the predictive capacity of models is exchangeable across different contexts, and it improves dramatically once data on university performance becomes available. We find that the models trained in the Dutch context have a better ability to identify dropouts in the Italian context than the other way around. Models trained on Dutch data allow us to better understand the relationship between educational credits obtained, the most important variable across models, and students' dropout. This study contributes to creating a European common arena for discussing Higher Education success issues. [ABSTRACT FROM AUTHOR]