by Helen MacGillivray
Despite much good work in teaching and learning, there are some fundamental problems, particularly in STEM disciplines and foundational learning across disciplines, that continue to re-emerge no matter how much they are ‘swept under the carpet’. Three are highlighted here.
The mixed messages given to many early career staff about balancing research and teaching are far more hypocritical than 40 years ago, and even go so far sometimes as to encourage irresponsibility in teaching. Many junior academics aspire to combine teaching and research, and universities must give genuine and non-hypocritical support to achieve this.
All postgraduate training should have an integrated, authentic and non-trivial component of learning to university teach, in the form of mentored experience, deep discipline understanding, training and internships; the concept of authentic work integrated learning should be applied to learning to university teach. Such teaching skills are invaluable in all workplaces not only academia.
My third point relates to my own discipline of statistics but in fact refers to all disciplines, particularly in this era of big data, data science and data analytics in which statistical thinking and the foundational teaching of statistics across disciplines are of even greater importance than ever before. The disastrous side-effects of ‘silo-funding’ and ‘empire-building’ must be counteracted by genuine rewarding of authentic effective collaboration between cutting-edge expertise in teaching statistics and curricula in other disciplines. Ignoring or relegating the former creates a ‘closed shop’ which repeats and perpetuates the inappropriate teaching and lack of understanding of many decades ago.
Professor Helen MacGillivray PFHEA
President, International Statistical Institute (ISI)
Chair, UN Global Network of Institutions for Statistical Training (GIST)
Editor, Teaching Statistics
2006 Australian Senior Learning and Teaching Fellow
Adjunct Professor QUT
ALTF 2019 Legacy Report here