Several universities are witnessing an increase in students’ enrolment in mathematics-intensive programmes over the last decades. This increase has come with the price of high failure rates in foundational mathematics courses, which poses challenges to mathematics teaching and learning in higher education. It is therefore inevitable, for some universities, to transform the teaching and learning of mathematics to more student-centred approaches that engage the students mathematically and enhance their success rates. We approach this transformative effort by investigating students’ perception of teaching, feedback, and assessment as a first step in reforming the teaching of a first-year mathematics course at a Norwegian university. The results of both quantitative and qualitative analyses of the data generated using a questionnaire from 107 (80 men) engineering students show that the status quo of teachings offers little support for learning. The teaching is dominated by teacher-led instruction, note-taking, and large pieces of proof which make learning difficult for students during class activities. The results also show that the current structure of the course offers limited formative feedback to students and that the assessment tasks require restructuring to capture students’ time and effort. We discuss the implications of these findings and make some recommendations for improvement.

The successful realisation of Industry 5.0 relies on seamless collaboration between humans and robots in shared workspaces. This paper investigates predictive human trajectory classification (HTC) in a factory setting to enhance safety, productivity, and adaptability in human-machine environments. Using a state-of-the-art light detection and ranging (LiDAR) system with reflective mirrors to extend the field of view, motion data are collected across a 26 m × 20 m indoor industrial floor. Human trajectories are classified to predict workstation transitions using a stacked long-short term memory (LSTM) network. To address the challenges of limited real-world data, transfer learning is applied by first training the model on synthetically generated trajectories. The paper compares a traditional stochastic method, specifically the Ornstein-Uhlenbeck (OU) process, with a modern learning based method, represented by a timeseries generative adversarial network (TimeGAN). Further on, the algorithm is fine-tuned on the real-world dataset. The comparison shows that in data-scarce scenarios, OU outperforms TimeGAN in most metrics. The LSTM model trained on OU-based data and real-world data shows an overall accuracy gain of 4.8 percentage points compared to training on real-world data only. It is able to classify the goal workstation three seconds in advance with an accuracy of 95.6%, demonstrating the potential for predictive models to improve workplace operations by reducing stall time, optimising workstation readiness, and reducing the need for traditional safety cages.

Selecting interior wood materials requires balancing functional and aesthetic demands with indoor air quality, as wood can emit volatile organic compounds (VOCs) and very volatile organic compounds (VVOCs) that either peak shortly after installation or persist over time. This study investigated time-resolved VOC and VVOC accumulation concentrations from untreated pine, spruce, aspen, and oak, representing wood species commonly used in Norway. Concentrations of VOCs and VVOCs were monitored for 25–28 days under low-ventilation, worst-case chamber conditions using Proton Transfer Reaction–Time-of-Flight Mass Spectrometry (PTR-TOF-MS). Emission behaviour was assessed using peak concentration, decay rate, half-time, stabilised concentration, cumulative exposure (AUC₂₈), and available health-based thresholds. Clear species-dependent differences were observed. Pine was the dominant VOC source, with the highest peak concentration, stabilised concentration, and AUC₂₈, mainly due to monoterpenes, which remained above the health-based threshold throughout the test period. Oak showed the second-highest VOC burden and a distinct acetate-dominated profile, with acetic acid showing the longest predicted exceedance duration. Aspen and spruce had lower VOC burdens, but their cumulative VVOC exposure exceeded VOC exposure. VVOCs generally peaked earlier than VOCs, although persistence varied by species. Overall, the concentration of VOCs and VVOCs was controlled by species, compound chemistry, volatility, and internal transport, supporting species- and compound-specific ventilation and material-selection strategies.