Whilst investigating student performance in design and arithmetic tasks, as well as during exams, electrodermal activity (EDA)-based sensors have been used in attempts to understand cognitive function and cognitive load. Limitations in the employed approaches include lack of capacity to mark events in the data, and to explain other variables relating to performance outcomes. This paper aims to address these limitations, and to support the utility of wearable EDA sensor technology in educational research settings. These aims are achieved through use of a bespoke time mapping software which identifies key events during task performance and by taking a novel approach to synthesizing EDA data from a qualitative behavioral perspective. A convergent mixed method design is presented whereby the associated implementation follows a two-phase approach. The first phase involves the collection of the required EDA and behavioral data. Phase two outlines a mixed method analysis with two approaches of synthesizing the EDA data with behavioral analyses. There is an optional third phase, which would involve the sequential collection of any additional data to support contextualizing or interpreting the EDA and behavioral data. The inclusion of this phase would turn the method into a complex sequential mixed method design. Through application of the convergent or complex sequential mixed method, valuable insight can be gained into the complexities of individual learning experiences and support clearer inferences being made on the factors relating to performance. These inferences can be used to inform task design and contribute to the improvement of the teaching and learning experience.

Poly(L-lactic acid) (PLLA) and iron (Fe) are popular bioresorbable material candidates for biomedical implants. However, PLLA coronary stents are relatively too thick compared to metallic stents when providing the same mechanical strength, while iron degrades too slowly. Recent studies show that PLLA coatings can enhance iron’s corrosion rate, and iron has strong mechanical strength, making PLLA–Fe composites ideal for bioresorbable implants. Although PLLA coatings on iron samples have been studied, research on embedding iron wires in relatively thick PLLA matrices is limited. Moreover, no studies have yet explored 3D-printed metal wire-reinforced PLLA monofilaments for biomedical applications. To address these research gaps and investigate the in vitro degradation profile of PLLA/Fe wire monofilaments for bioresorbable stents, this study first developed a novel polymer filament–metal wire coextrusion 3D printer for printing PLLA/Fe wire monofilaments. In vitro degradation tests were then conducted on both PLLA/Fe and neat PLLA monofilaments at 50 °C. Thereafter, characterizations, including mass loss, pH, surface appearance and morphology, tensile tests, gel permeation chromatography (GPC), and differential scanning calorimetry (DSC), were performed. Results indicated that the overall degradation rate of PLLA/Fe monofilaments was higher than that of PLLA counterparts, while the degradation rate of PLLA matrix was not affected by the embedded iron wire according to molecular weight analysis. Notably, the Young’s modulus and stiffness of PLLA monofilaments were significantly improved by the iron wires during the early stages of degradation, but the reinforcement in tensile strength was negative after immersion due to the poor embedding quality of the iron wires in the PLLA monofilaments. With future improvement of the embedding quality of iron wire, the 3D-printed PLLA/Fe wire composites can have great potential in the development of biomedical devices using the novel 3D printing method, including most types of stents and bone scaffolds.