Abstract
Online masters’ program chairs need up-to-date information to monitor efficiently and effectively all the courses in the program for which they are responsible. Learning Management Systems supporting the operation of the online programme collect vast amounts of data about the learning process. These systems are geared to support individual teachers and students, not program chairs. This article presents the process that led to the development of a Dashboards for program chairs, based upon an analysis of their regular supervision tasks, decision-making information needs, and available data in the learning management system, Moodle. The information presented via the dashboard is aggregated and contextualised for all students enrolled in the program, in all its courses, contributing to improve decision-making in program chairing. The dashboard prototype is presented as a concrete outcome of this process, which can be replicated to achieve more advanced and updated versions, hopefully contributing to better program chairing. © 2021, Springer Nature Switzerland AG.

Abstract

Abstract
Museums continue to exert fascination in their visitors. However, the new generation of visitors expects museological experiences that promote their active participation. It is in this context that games and the gamification of such experiences capitalize on experiential learning by experimenting and enacting with in-game embedded artefact surrogates and know-how. In this article, we present four distinct projects that aim to enhance the visitors' experience in museums and green spaces, and also their effectiveness in informal learning. In the first project, gamification is used in combination with Augmented Reality to provide a more engaging experience in a boat museum. The drive of this experience is the metaphor of the stickers album collection to unleash the relevant information of the key-artefacts of the museum collection. The second and third projects focus on the use of pervasive games, more specifically location-based games, to enhance the visitors' experience and informal learning in a natural park and a botanical garden, respectively. The second project presents the concept of a mobile app for outdoor nature experiences. The drive for the experience in the third project is the narrative that intertwines specific locations in the botanic garden and a story inspired by the same place. Finally, in the fourth project, we focus on the potential of technology to provide accessibility in museums for people with special needs or disability, focusing more specifically on blind visitors. Copyright © 2020 for this paper by its authors.

Abstract
Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.

Abstract
Background: Internet-based cognitive-behavioral therapy (iCBT) is more effective when it is guided by human support than when it is unguided. This may be attributable to higher adherence rates that result from a positive effect of the accompanying support on motivation and on engagement with the intervention. This protocol presents the design of a pilot randomized controlled trial that aims to start bridging the gap between guided and unguided interventions. It will test an intervention that includes automated support delivered by an embodied conversational agent (ECA) in the form of a virtual coach. Methods/design: The study will employ a pilot two-armed randomized controlled trial design. The primary outcomes of the trial will be (1) the effectiveness of iCBT, as supported by a virtual coach, in terms of improved intervention adherence in comparison with unguided iCBT, and (2) the feasibility of a future, larger-scale trial in terms of recruitment, acceptability, and sample size calculation. Secondary aims will be to assess the virtual coach's effect on motivation, users' perceptions of the virtual coach, and general feasibility of the intervention as supported by a virtual coach. We will recruitN = 70 participants from the general population who wish to learn how they can improve their mood by using Moodbuster Lite, a 4-week cognitive-behavioral therapy course. Candidates with symptoms of moderate to severe depression will be excluded from study participation. Included participants will be randomized in a 1:1 ratio to either (1) Moodbuster Lite with automated support delivered by a virtual coach or (2) Moodbuster Lite without automated support. Assessments will be taken at baseline and post-study 4 weeks later. Discussion: The study will assess the preliminary effectiveness of a virtual coach in improving adherence and will determine the feasibility of a larger-scale RCT. It could represent a significant step in bridging the gap between guided and unguided iCBT interventions.

Abstract
Index structures are fast-access methods. In the past, they were often used to minimise fetch operations to external storage devices (secondary memory). Nowadays, this also holds for increasingly large amounts of data residing in main-memory (primary memory). Examples of software that deals with this fact are in-memory databases and mobile device applications. Within this scope, this paper focuses on index structures to store, access and delete interval-based time-dependent (temporal) data from very large datasets, in the most efficient way. Index structures for this domain have specific characteristics, given the nature of time and the requirement to index time intervals. This work presents an open-source time-efficiency focused variant of the original Interval B+ tree. We designate this variant Improved Interval B+ tree (I2B+ tree). Our contribution adds to the performance of the delete operation by reducing the amount of traversed nodes to access siblings. We performed an extensive analysis of insert, range queries and deletion operations, using multiple datasets with growing volumes of data, distinct temporal distributions and tree parameters (time-split and node order). Results of the experiments validate the logarithmic performance of these operations and propose the best-observed tree parameter ranges.