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This empirical work-in-progress paper examines the impact of two technology-enhanced learning tools, an interactive visualization with multiple representations and explicit scaffolding and a simulation tool with single representation and implicit scaffolding, on students’ conceptual understanding and affective outcomes in semiconductor physics. This domain is known for early conceptual difficulty that often limits student progression into advanced coursework and the semiconductor workforce. Using a crossover design, 20 undergraduate electrical engineering students engaged with both tools and completed pre and post-assessments of conceptual knowledge, perceived understanding, interest, and motivation. A semi-structured focus group was analyzed using thematic analysis to capture students’ learning experiences. The findings reveal that while both tools supported students' cognitive and affective processes, one primarily fostered foundational understanding for novice learners, whereas the other one enabled deeper exploration for more experienced students. Although limited by sample size and short duration, the results suggest that aligning tool design with learners’ developmental stage may support both learning and motivation. Overall, this study demonstrates how instructional design features shape students’ engagement with complex engineering content and underscores the importance of adaptive technology-enhanced learning environments. 

This empirical work-in-progress paper examines the impact of two technology-enhanced learning tools, an interactive visualization with multiple representations and explicit scaffolding and a simulation tool with single representation and implicit scaffolding, on students’ conceptual understanding and affective outcomes in semiconductor physics. This domain is known for early conceptual difficulty that often limits student progression into advanced coursework and the semiconductor workforce. Using a crossover design, 20 undergraduate electrical engineering students engaged with both tools and completed pre and post-assessments of conceptual knowledge, perceived understanding, interest, and motivation. A semi-structured focus group was analyzed using thematic analysis to capture students’ learning experiences. The findings reveal that while both tools supported students' cognitive and affective processes, one primarily fostered foundational understanding for novice learners, whereas the other one enabled deeper exploration for more experienced students. Although limited by sample size and short duration, the results suggest that aligning tool design with learners’ developmental stage may support both learning and motivation. Overall, this study demonstrates how instructional design features shape students’ engagement with complex engineering content and underscores the importance of adaptive technology-enhanced learning environments. 

Sanitizing sensitive text data typically involves removing personally identifiable information (PII) or generating synthetic data under the assumption that these methods adequately protect privacy; however, their effectiveness is often only assessed by measuring the leakage of explicit identifiers but ignoring nuanced textual markers that can lead to re-identification. We challenge the above illusion of privacy by proposing a new framework that evaluates re-identification attacks to quantify individual privacy risks upon data release. Our approach shows that seemingly innocuous auxiliary information -- such as routine social activities -- can be used to infer sensitive attributes like age or substance use history from sanitized data. For instance, we demonstrate that Azure's commercial PII removal tool fails to protect 74\% of information in the MedQA dataset. Although differential privacy mitigates these risks to some extent, it significantly reduces the utility of the sanitized text for downstream tasks. Our findings indicate that current sanitization techniques offer a \textit{false sense of privacy}, highlighting the need for more robust methods that protect against semantic-level information leakage. 

The sealing potential of a caprock is critical for the long-term success of geologic carbon storage and can be influenced by lithological heterogeneities and structural discontinuities (e.g., bedding planes, fractures, and faults). CO2LPIE project investigates the field-scale sealing potential of heterogeneous Opalinus Clay formation under periodic CO2-water injection. Laboratory experiments are conducted on specimens representing the shaly and sandy facies extracted from potential injection sites. It appears that the sandy facies, due to their greater fraction of quartz and carbonate minerals, exhibit higher drained and unjacketed bulk moduli than the shaly facies. The sandy specimens display permeability a few times higher than the shaly counterparts due to differences in pore-scale characteristics. The permeability values of both facies remain well below the threshold required for an effective caprock. A power-law porosity-permeability relationship with sensitivity exponent values of 17-18 is identified, highlighting that even minor porosity variations can significantly change the fluid flow through Opalinus Clay. While existing models capture general permeability trends, they fail to represent this extreme sensitivity to porosity changes. To improve predictability at the field scale, future work will integrate these laboratory findings with numerical simulations and in-situ experiments, advancing the understanding of caprock integrity under CO2 injection conditions.