The Digiphyslab Project: Digital physics laboratory work for on-campus and distance learning

EXPERIMENTAL WORK AND COVID-19 With the emergence of the COVID-19 in spring 2020, physics teaching at university level needed to be rapidly transformed into a distance learning mode all around the world. While lectures and tutorials could rather easily be substituted with video conferences, self-study materials, or recorded videos, transforming a hands-on laboratory course into distance learning is much more challenging facing its traditional structures, manifold learning objectives, and the essential use of typical laboratory equipment (Hut et al., 2020; Jelicic et al., 2022; Werth et al., 2021).  DIGITAL TECHNOLOGIES AS A PROMISING APPROACH A promising approach to develop laboratory courses especially, and to offer these courses in a distance learning mode, is to use digital technologies like smartphones. Smartphones are widely used, often cheaper than traditional equipment and allow convenient data collection and analysis by utilising built-in sensors. Thus, smartphones provide an affordable opportunity to conduct experiments beyond the laboratory. Additionally, they can enhance inquiry-based learning processes due to the reduction of students’ extraneous cognitive load (Becker et al., 2020). THE DIGIPHYSLAB-PROJECT The DigiPhysLab-project (Lahme et al., in press), co-funded by the European Union, follows this approach of utilising digital technologies like smartphones for physics experiments by developing 15 high-quality, competence-centered experimental tasks that can therefore be implemented either in on-campus or distance learning settings. All developed tasks are linked to a theoretical framework for design principles of experimental tasks and evaluated with students at the participating universities. The task instructions and further materials are published as open educational resources on the project website (www.jyu.fi/digiphyslab). In the presentation, the framework, the tasks, and the evaluation scheme are presented, and the usability of the tasks is discussed. REFERENCES Becker, S., Klein, P., Gößling, A., & Kuhn, J. (2020). Using mobile devices to enhance inquiry-based learning processes. Learning and Instruction, 69, 101350. Hut, R. W., Pols, C. F. J., & Verschuur, D. J. (2020). Teaching a hands-on course during corona lockdown: from problems to opportunities. Physics Education, 55(6), 065022. Jelicic, K., Geyer, M. A., Ivanjek, L., Klein, P., Küchemann, S., Dahlkemper, M. N., & Susac, A. (2022). Lab courses for prospective physics teachers: what could we learn from the first COVID-19 lockdown? What could we learn from the first COVID-19 lockdown? European Journal of Physics, 43(5), 55701. Lahme, S. Z., Klein, P., Lehtinen, A., Müller, A., Pirinen, P., Susac, A., & Tomrlin, B. (in press). DigiPhysLab: Digital Physics Laboratory Work for Distance Learning. PhyDid B - Didaktik der Physik - Beiträge zur DPG-Frühjahrstagung - online 2022. Werth, A., Hoehn, J. R., Oliver, K., Fox, M. F. J., & Lewandowski, H. J. (2021). Rapid Transition to Remote Instruction of Physics Labs During Spring 2020: Instructor Perspectives. arXiv. https://doi.org/10.48550/arXiv.2112.1225

Large Impacts of Climatic Warming on Growth of Boreal Forests since 1960

Peer reviewe

A framework for designing experimental tasks in contemporary physics lab courses

While lab courses are an integral part of studying physics aiming at a huge variety of learning objectives, research has shown that typical lab courses do not reach all the desired goals. While diverse approaches by lab instructors and researchers try to increase the effectiveness of lab courses, experimental tasks remain the core of any lab course. To keep an overview of these developments and to give instructors (and researchers) a guideline for their own professional efforts at hand, we introduce a research-informed framework for designing experimental tasks in contemporary physics lab courses. In addition, we demonstrate within the scope of the EU-co-funded DigiPhysLab-project how the framework can be used to characterize existing or develop new high-quality experimental tasks for physics lab courses.Comment: 10 pages, 1 figure, 1 table, submitted to GIREP 2022 proceedings, minor revisions especially in Sec. 3 after revie

High-resolution analysis of observed thermal growing season variability over northern Europe

Strong historical and predicted future warming over high-latitudes prompt significant effects on agricultural and forest ecosystems. Thus, there is an urgent need for spatially-detailed information of current thermal growing season (GS) conditions and their past changes. Here, we deployed a large network of weather stations, high-resolution geospatial environmental data and semi-parametric regression to model the spatial variation in multiple GS variables (i.e. beginning, end, length, degree day sum [GDDS, base temperature + 5 degrees C]) and their intra-annual variability and temporal trends in respect to geographical location, topography, water and forest cover, and urban land use variables over northern Europe. Our analyses revealed substantial spatial variability in average GS conditions (1990-2019) and consistent temporal trends (1950-2019). We showed that there have been significant changes in thermal GS towards earlier beginnings (on average 15 days over the study period), increased length (23 days) and GDDS (287 degrees C days). By using a spatial interpolation of weather station data to a regular grid we predicted current GS conditions at high resolution (100 m x 100 m) and with high accuracy (correlation >= 0.92 between observed and predicted mean GS values), whereas spatial variation in temporal trends and interannual variability were more demanding to predict. The spatial variation in GS variables was mostly driven by latitudinal and elevational gradients, albeit they were constrained by local scale variables. The proximity of sea and lakes, and high forest cover suppressed temporal trends and inter-annual variability potentially indicating local climate buffering. The produced high-resolution datasets showcased the diversity in thermal GS conditions and impacts of climate change over northern Europe. They are valuable in various forest management and ecosystem applications, and in adaptation to climate change.Peer reviewe

High-resolution analysis of observed thermal growing season variability over northern Europe

Strong historical and predicted future warming over high-latitudes prompt significant effects on agricultural and forest ecosystems. Thus, there is an urgent need for spatially-detailed information of current thermal growing season (GS) conditions and their past changes. Here, we deployed a large network of weather stations, high-resolution geospatial environmental data and semi-parametric regression to model the spatial variation in multiple GS variables (i.e. beginning, end, length, degree day sum [GDDS, base temperature + 5 degrees C]) and their intra-annual variability and temporal trends in respect to geographical location, topography, water and forest cover, and urban land use variables over northern Europe. Our analyses revealed substantial spatial variability in average GS conditions (1990-2019) and consistent temporal trends (1950-2019). We showed that there have been significant changes in thermal GS towards earlier beginnings (on average 15 days over the study period), increased length (23 days) and GDDS (287 degrees C days). By using a spatial interpolation of weather station data to a regular grid we predicted current GS conditions at high resolution (100 m x 100 m) and with high accuracy (correlation >= 0.92 between observed and predicted mean GS values), whereas spatial variation in temporal trends and interannual variability were more demanding to predict. The spatial variation in GS variables was mostly driven by latitudinal and elevational gradients, albeit they were constrained by local scale variables. The proximity of sea and lakes, and high forest cover suppressed temporal trends and inter-annual variability potentially indicating local climate buffering. The produced high-resolution datasets showcased the diversity in thermal GS conditions and impacts of climate change over northern Europe. They are valuable in various forest management and ecosystem applications, and in adaptation to climate change.Peer reviewe