Measuring Students' Reading Behavior with an Ambulatory Assessment - A Field Report on a Smartphone-Based Reading Diary Study

In prior research, reading behavior was predominantly measured using either a question­naire, which is economical and easy to implement but imprecise, or paper-pencil diaries that document reading behavior quite accurately, but which are time consuming and costly. The present study aims to introduce and evaluate a precise and easy to implement mea­sure of reading behavior, namely a reading diary app in which participants can record their reading behavior on a smartphone. To evaluate the development procedure, the first research question asked whether data gathered with the app is of high quality (e.g., reliabil­ity). The second research question asked how reading time recorded via the app is related to reading time assessed via different retrospective questionnaires. n = 31 German university students recorded their reading activities for 14 days. Different approaches were applied to estimate the data quality and reliability and yielded satisfactory results. Participants reported more time spent reading daily on the retrospective questionnaire than when re­cording their reading time using the app. The correlation between reading diary app data and questionnaire data was medium in size. Our findings are discussed in the light of future directions for reading research and the use of ambulatory assessments

Kinder in den ersten drei Lebensjahren in altersgemischten Gruppen. Anforderungen an frühpädagogische Fachkräfte. Eine Expertise der Weiterbildungsinitiative Frühpädagogische Fachkräfte (WiFF)

Worauf müssen sich frühpädagogische Fachkräfte vorbereiten, wenn sie ein- und zweijährige Kinder in altersgemischte Gruppen aufnehmen? In dieser [...] Expertise wird dieser Frage ausführlich nachgegangen. Neben Hinweisen auf das entwicklungspsychologische Grundlagenwissen, werden die unterschiedlichen Modelle der Altersmischung beschrieben und ihre Konsequenzen für die Praxis diskutiert. Mit Empfehlungen für die Gestaltung eines altersgemischten Alltags in der Kindertageseinrichtung und mit einer Checkliste für eine pädagogisch fundierte Altersmischung trägt diese Expertise zur Orientierung von Weiterbildnerinnen und Weiterbildnern, Fachkräften und Trägern bei. (DIPF/Orig.

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Digitaler Wandel als Chance für die Forschung? Einblicke in eine Smartphone basierte Lesetagebuch Studie

Poster präsentiert auf der Tagung der Gesellschaft für Empirische Bildungsforschung (GEBF), Bamberg

Using an ambulatory assessment to measure students’ reading behavior: Findings from a smartphone based reading diary study

paEpsy-Konferenz, Leipzig, 09.-12.09.201

Using an ambulatory assessment to measure students’ reading behavior: Findings from a smartphone based reading diary study

ZIB-Akademie, Bernried, 8.-12. Juli 201

Using an ambulatory assessment to measure students’ reading behavior: Findings from a smartphone based reading diary study

ZIB-Akademie, Bernried, 8.-12. Juli 201

Digitaler Wandel als Chance für die Forschung? Einblicke in eine Smartphone basierte Lesetagebuch Studie

Poster präsentiert auf der Tagung der Gesellschaft für Empirische Bildungsforschung (GEBF), Bamberg