Skills Educ8n

Students likely need ‘skills’ (e.g., expertise, confidence & motivation) for numerous purposes. But, given extents of STEM-SE harms, STEPWISE prioritizes skill development like those illustrated at right/below for students’ eventual self-led RiNA projects to overcome ST[EM]SE harms of their concern. For citizen autonomy, STEPWISE prioritizes helping students to increasingly gain control over procedures and conclusions (see Lock, 1990, model) of their activities. Such self-led knowledge generation & actions can be compromized when teachers and/or textbooks provide extensive guidance – something that seems prevalent where there is intense pressure to orient science/STEM education towards selection & education of potential knowledge producers like engineers, scientists, etc. To help promote citizen autonomy, as with all learning outcomes, we recommend uses of our 3-phase, constructivism-informed, schema for such skill development. Resources below are based on STEPWISE, but have largely been informed by my old Skills Apprenticeship Handbook – which I suggest needs to be updated to promote skills learning in STEM-SE contexts.

Science and engineering may be thought of as different fields. As indicated at right/below, for example, many believe that their goals differ – with scientists aiming to document & explain existing ’cause’ (C) –> result’ (R) relationships, while engineers (& technologists) seek to invent new C–>R relationships. Roth (2001) suggested, however, that – as illustrated below (and elaborated here) – ‘science’ & ‘technology’ in STEM co-affect each other and, so, perhaps have much in common or may be considered hybrid.

For science inquiries and for technology/engineering design projects, it is common for investigators to explore – as illustrated at right/below – possible influences of changes in one or more independent (’cause’) variables on one or more dependent (‘result’) variables and, perhaps eventually, provide a possible explanation (‘hypothesis’) of why two or more of the variable sets are related in certain ways. Among ways to choose such variable combinations, people can propose relationships between lists of variables given here. They might suggest decreased sleep time will reduce human memory and hypothesize that this may be due to build-up of toxins in the brain. Similarly, in engineering design, they may suggest that decreases in wing density (e.g., with added air pockets) should increase airplane fuel economy because of increased wing lift.

With reference to the above stereotypical model of skills & processes of science & technology, predictions & hypotheses can be tested empirically either with experiments or correlational studies. As shown at right/below, experiments involve monitoring results of ‘forced’ changes in ’cause’ (independent) variables while studies involve inquiries into possible causal correlations between naturally-changing independent and dependent variables. As well, changed amounts of the independent variable in experiments usually have regular intervals (e.g., 0, 5, 10^oC, etc.), but those in studies are typically collected in irregular intervals (and, so, must be added to graphs rearranged to have regular intervals for the independent variable). Choices between the two processes often depend – as elaborated here – on practicalities & ethics. In inquiries into STSE Harms that may occur in RiNA projects, although investigators may use experiments, they may most-ethically choose correlational studies if experimentation would generate harmful results (e.g., a study of increased cigarette smoking as may affect amounts of finger discolouration).

As indicated in the above stereotypical model of processes of science & technology, technological invention (with reasons for designs) may be comparable to predictions about changes in dependent variables that may result from changes in independent variables in science inquiries. In science inquiries, investigators often attempt to ‘control’ (keep unchanged) all independent variables except one; but, in technological design, engineers often change several independent variables at once – to, in a sense, determine a ‘recipe’ of several variable changes that may give rise to optimal sets of results. As shown at right/below, for instance, making a suitable tent may require a balance of uses of certain chemicals, fibre weaving & materials. Such designs are complex because changes in some independent variables can positively affect changes in some dependent variables, but negatively affect others. Such compromises can be problematic; e.g., when waterproofing is more valued than chemical toxicity. Additional theory about engineering design education is available here.