{"id":1884,"date":"2023-09-19T17:43:31","date_gmt":"2023-09-20T00:43:31","guid":{"rendered":"https:\/\/blogs.ubc.ca\/eoasquest\/?page_id=1884"},"modified":"2023-12-06T18:53:50","modified_gmt":"2023-12-07T01:53:50","slug":"desirable-skills","status":"publish","type":"page","link":"https:\/\/blogs.ubc.ca\/eoasquest\/desirable-skills\/","title":{"rendered":"Desirable skills"},"content":{"rendered":"

This page is the summary section from the complete report<\/a>. <\/strong><\/p>\n

This section is about desirable skills<\/strong>. The recommendations sections are about desirable tactics<\/strong> for attaining (and fostering) those skills.<\/p>\n

Summary<\/h1>\n

NOTE: when the terms such as \u201cEarth sciences\u201d or \u201cgeoscience\u201d are used, they are meant to encompass all geologic, atmospheric, ocean, environment and climate sciences. <\/strong><\/p>\n

Capabilities and attributes that students should<\/strong> be able to demonstrate upon graduation from a quantitatively oriented Earth science (QES) program are described and discussed. This is not about the current<\/strong> QES curriculum in the department of EOAS. That is described on the current QES course content<\/a> page. Options and preferences are based on input from interviews, surveys and discussions with EOAS faculty and students, from peers and peer institutions, and from literature about career preparation from academic and employer perspectives.<\/p>\n

The QuEST project focuses on quantitative earth sciences, and the desirable skills here reflect that. However, the “employability” of a student depends at least as much on abilities and attributes that are not unique to a discipline. Saunders and Zuzel, 2010<\/a>, quote a useful definition of employability as “a set of achievements – skills, understandings and personal attributes – that make graduates more likely to gain employment and be successful in their chosen occupations, which benefits themselves, the workforce, the community and the economy<\/em>.\u201d Usher, 2022<\/a>, discusses some aspects of why it seems difficult for institutions to anticipate market demand \u2013 i.e. to define how best to prepare society\u2019s future professionals. Four basic (and perhaps obvious) aspects are (a) the future is hard to predict, (b) companies, governments and universities or colleges do not have the same priorities, (c) companies are precise about skills they want when hiring, but most of those skills arise from experience<\/em> not academic learning<\/em> and (d) academics (and institutions) are slow and sometimes reticent to change. Therefore, the majority of desirable skills (with a few exceptions) are considered “generic” rather than targeting specific careers or disciplines.<\/p>\n

A slightly more “academic” articulation of forward-looking goals for a university-based undergraduate education is offered by Kwok, 2018<\/a> (short, clear, and well worth reading): “A lot of factual information is available on the Internet, and artificial intelligence is making certain occupations obsolete. It is therefore much more important to give our students fundamentals that will stay with them for the rest of their lives. These essential tools include language skills such as comprehension, expression, and communication, as well as quantitative skills such as analysis, seeing hidden patterns, identifying variables, and formulating solutions to problems.<\/strong><\/em>” Note that “fundamental skills<\/em>” here does not refer to “basic theory of a discipline” – it means well-educated, careful, creative and clear ways of thinking.<\/p>\n

To reflect the distinction between discipline specific capabilities and the more general characteristics of highly employable graduates, desirable skills identified below are organized into three main categories; (a) knowledge, skills and attitudes that are essentially quantitative (i.e. math, physics and computation), (b) those that are unique to the geosciences, and (c) capabilities associated with a person\u2019s readiness to enter the workforce or advanced education.<\/p>\n

Preparing highly employable graduates is not only a responsibility of the Department, but successful graduates are also important from the recruiting or “marketing” perspective.<\/p>\n

<\/a>1.1 Regarding quantitative capabilities<\/h2>\n

One persistent theme is the importance of \u201chigher-level\u201d aspects of QES learning, i.e. the need to nurture critical thinking involving quantitative and data-oriented information. It takes time and guided practice to gain the necessary maturity to make reliable decisions and judgements based on physics, data sets, and mathematical models.<\/p>\n

Quantitative \u201ctopics\u201d are easy to list and they seem relatively consistent across QES degree programs. However, rather than listing topics like \u201cODEs\u201d or \u201clinear algebra\u201d, the important concepts would be more usefully described in terms of the scope, context and expected level of mastery. The department\u2019s research expertise will inform these contexts and those chosen should span the degree specializations, however, students also need to experience settings that reflect the broader world of work.<\/p>\n

Computing should be integrated throughout quantitative courses and curriculum. This reflects the growing recognition of computing as a fundamental form of “literacy” across professions (e.g. Guzdial, 2019)<\/a>. A balance is needed between the development of coding skills versus quantitative knowledge that uses computing codes. These two \u201cgoals\u201d are not the same and mixing them can compromise success.<\/p>\n

AI\/ML (including generative AI) is rapidly emerging as a necessary component of thinking across the STEM disciplines. Forward looking curriculum must include opportunities for students to gain mature and well-informed capabilities involving AI.<\/p>\n

Sources that consider desirable skills more generally for geoscience graduates are not focused on smaller quantitative programs, but they do remind us that basic numerical capabilities should not be lost in the advanced math; capabilities such as data visualization, managing uncertainty, informed use of statistics, wrangling large and\/or public data sets, etc.<\/p>\n

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<\/a>Collected quantitative capabilities<\/span><\/span><\/h3>\n

To be selected or adjusted and specified with contexts & levels of mastery, appropriately for the specific degree specialization.<\/span><\/p>\n