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100 Years of Academy Education: Changes and Trends

Tomorrow's Academy

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Could it be that nowadays, over-focusing on problem solving skills and domain-specific toolsets, academic education produces, to a larger extent, narrow specialists, less capable of crossing professional domain boundaries, seeing general cross-domain trends and concepts? 


The message below "touches on a discussion about some general challenges of higher education (as seen both from the academy and from industry) and desired improvements in teaching “problem definition skills” through more focus on “meta-professions”.  It is by Michael Reitman, PTC Inc., Haifa, Israel ( and Orit Hazzan, Faculty of Education in Science and Technology, Technion - Israel Institute of Technology ( It is from the article, “Meta-Professions: Cross-Domain and Domain-Agnostic Teaching” 




Rick Reis

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Tomorrrow's Academy

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100 Years of Academy Education: Changes and Trends


The past 100 years have been the period of drastic changes in the system of academic education over the world.


* Unprecedented growth of high-education students that came along with a parallel unprecedented growth in [the] number of universities and academic staff.


* "Quantum leap” in the material base of universities (labs, technology, research facilities), " especially in the last 50 years. 


* Huge increase of candidates which led to increase in the acceptance criteria of top universities,  which may have become stricter than before.


At the same time, the question often raised is why there is a general impression that university graduates 100 years ago generally had a wider education and a stronger ability to efficiently accommodate and progress in any professional sub-domain or even in neighboring / related domains?

Naturally, the graduates in the early 20th century did not have opportunities to learn countless concepts and technologies that nowadays graduates can (and should) acquire.  But, as a result of this “smaller toolbox”, were they worse engineers, doctors, teachers? The feeling is the opposite – they were, to a large extent, better, stronger professionals, less locked in domain-specific tools and technologies, capable of applying their skills to a wider field of professional practice.


Could it be that nowadays, over-focusing on problem solving skills and domain-specific toolsets, academic education produces, to a larger extent, narrow specialists, less capable of crossing professional domain boundaries, seeing general cross-domain trends and concepts?  And this unwanted trend seems to have increased over recent decades, while, at the same time, the need for graduates with wider, cross-domain perspectives has even widened.


Impact of Industry: Dual Message


The most crucial impact on the academy has been coming from industry – driven by revolutionary changes that only keep accelerating in recent decades.


In theory, such constantly transforming industry is supposed to encourage academic education to produce professionals capable of quickly adapting to changes, crossing domain boundaries and executing well in a wider range of professional fields. In practice, however, due to increasing specialization and overwhelming flow of domain-specific information in each professional field, industry is actively supporting and encouraging education of “narrowly-tailored” specialists who could join the workforce immediately upon graduation, with minimal extra training.


The industry’s influence on the academy comes both directly (sponsorship and scholarship for programs providing domain-specific skills and tools) and indirectly (“narrow specialized” graduates have better chances to quickly find a position in the job market). This indirect influence is imposed during the recruiting process in industry, when a job candidate is usually tested for existing knowledge rather than for the capability to learn (the latter tests are much costlier and harder to define/execute, and therefore are seldom used). Companies are rarely willing to accept and train people lacking domain-specific knowledge (e.g., coming from different professional domain), they are looking for “ready to start” specialists who require minimal investment on training.


Thus, the academy has adapted to such industry requirements, and the education process is designed to focus on providing domain-specific knowledge and problem solving skills for specific industries – to ensure that graduates can be easily integrated into the modern workforce.

”Problem Solving” vs “Problem Definition” Skills  

One immediate pedagogical consequence of the industry influence on the academic process is the excessive focus on problem solving skills (and special focus on domain-specific problem solving skills and tools). This often comes on account of knowledge management skills of wide applicability – ability to process and structure information, problem definition and self-learning skills. (For simplicity, we will refer to the latter group as “problem definition” skills.). These problem definition skills seem too “abstract”, not of immediate practical importance for future graduates and, naturally, less time and “mind set” is allocated for their teaching.


As a result, excessive focus on domain-specific problem solving skills may manifest itself in graduates that:


* are increasingly narrow-educated specialists

* have weaker problem definition skills

Problem Definition Skills: “Bottom-Up” Learning Process

Being less represented in academic programs (due to the lack of focus and under-developed methodology), problem-definition skills are mainly acquired implicitly, through research activity in [the] academy, or through application of problem solving skills to [a] variety of problems in industry.


One can say that problem definition skills are learned today mostly in [a] bottom-up manner – gradually acquiring general concepts and skills upon dealing with [a] wide range of specialized cases and applications.  

It is [a] long and poorly defined learning process (e.g., see [1]). One practical consequence of such bottom-up learning is that graduate students have better chances to fill the gap and acquire [a] good balance of problem solving and problem definition skills (due to longer years in [the] academy and deeper involvement in research activities).


It is noteworthy to observe that in professional domains where problem definition and self-learning skills are of critical importance (Medicine, Law, Business, and even Engineering), higher academic degrees have become the norm  (at least Master level). This can be evidence that industry also acknowledges the gap in problem definition skills in bachelor degree graduates.

MERge Pedagogical Model, Three Meta-Professions

MERge pedagogical model [2] leans on three meta-professions: Management, Education and Research.

The notion of meta-profession implies an organized set of domain-agnostic (or cross-domain) concepts and skills needed for the practitioner in any domain of human activity (Business, Engineering, Education, Medicine/Biology, etc.). In each domain there are specific concepts, methods and tools that realize (to a different extent and in a different manner) same meta-profession. These domain-specific concepts, methods and tools are often developed in separation, without any reference to similar methods and tools existing in other domains. As a result, same meta-profession may be over-developed in one professional domain, while under-developed in another…


MERge is a powerful meta-framework that can be implemented in various areas of human activity – both in the academy and in industry.


An interesting proposal for utilizing MERge framework in industry is suggested in [3].


Another promising direction could be usage of MERge as [a] conceptual foundation and framework for developing more comprehensive tests checking candidate’s learning, managing and research abilities – to complement the traditional knowledge-checking tests. Such MERge-based tests could have positive impact both on [the] recruiting process in industry and on candidate selection for academic studies. This direction needs further research and elaboration.


[The] most natural implementation of [the] MERge pedagogical model would be in the academy – to improve general structuring of education, [and] bring more focus on general concepts, tools and skills applicable across different domains. This can contribute to improved quality of education in every professional domain.


However, if MERge implementation is rolled out in separation in each professional domain, if each realization of meta-profession continues being taught separately, as today, then [the] impact of improved education structuring would be less significant.


We believe that cross-domain implementation of [the] MERge pedagogical model can realize its full potential in the academy, and that cross-domain teaching of meta-professions would allow addressing unwanted trends growing in academic education.


One positive effect from introducing MERge framework in the academy and bringing more focus to meta-professions could be increased opportunities for learning “problem definition” skills in [a] “top-down” manner, allowing undergraduate students to acquire these skills in parallel with learning domain-specific problem-solving skills and tools.

Cross-Domain and Domain-Agnostic Teaching of Meta-Professions

[The] main barriers restraining meta-profession teaching within [the] boundaries of specific domains  are (a) domain-specific vocabulary (terminology, roles, objectives, requirements), and (b) domain-specific scope of realization of meta-professions.


Two teaching strategies can be proposed to allow breaking through domain boundaries when teaching meta-professions: cross-domain teaching and domain-agnostic teaching.


Cross-domain teaching of meta-professions is based on one of two approaches:


(a) Peer Teaching (cross-domain courses, can be presented jointly by practitioners from different domains).This allows presenting concepts and tools developed and used in one professional domain and their possible synergy with corresponding concepts and tools in  another domain. It also allows both enriching domain-specific skills and implicitly exposing students to cross-domain concepts and skills. 


Such [a] teaching approach becomes more popular and more common (e.g., [5], [14]). Other evidence of growing understanding about the importance of cross-domain perspective in education can be the appearance of dedicated academic units focusing on interdisciplinary teaching (e.g., Worldview Stanford in Stanford University). 


b.  (b) Exposing students to concepts and skills of wide applicability through teaching application of domain-independent sciences to specific domain context.


·    Teach modern methods and concepts being developed in formal sciences (logic, mathematics, statistics, theoretical computer science, information theory, game / decision theory, and portions of linguistics) to students in application domains (business, engineering, medical, social sciences), focusing on potential application of these concepts and methods ([6], [8], [15]).


·    The opposite-- present domain-specific issues and problems (challenges encountered in business, engineering, medicine, sociology, etc.) to students specializing in abstract sciences (advanced math, algorithms, psychology, etc.) – to widen their understanding about real-life problems where wide applicability methods and concepts from their field of studies could potentially be applied.


·    Present [a] history of relevant professional domains focusing on analysis, general patterns and trends emerging from domain specific development of events (e.g., [7], [13]).

Domain-agnostic teaching of meta-professions implies more focus on domain-agnostic sciences and subjects. This could be some branches of philosophy (epistemology, logic), formal and mathematical logic (e.g., [8], [9]), special courses focused on thinking skills of wide applicability (e.g., [4], [10], [11], [12], [19]).


Table: Examples of teaching problem definition skills in [a] top-down manner


Cross-Domain Teaching

Domain-agnostic teaching


Courses on management tools and approaches – taught jointly by practitioners from different domains ([4])

Relevant topics from decision making theory,

Industrial and organizational psychology
([4], [14])


Presenting concepts and tools across different domains (peer teaching) [5], [14]

Topics of applied psychology relevant for professional domain ([16])

Courses focused on thinking skills of wide applicability ([10], [11], [12], [18], [19]);

Decision making topics; Educational Psychology ([4], [14], [17])


Advanced math topics for non-math students with focus on possible domain-specific application [6], [8], [15];

Courses to math students focusing on domain-specific concepts and challenges;

Topics in history of domain focusing on general trends and concepts [7], [13]

Philosophy topics (epistemology, etc.);

Logic (philosophical, formal, mathematical)

[8], [9]


Teaching meta-professions (or at least some meta-subjects) in [a] domain-agnostic or cross-domain manner is becoming more critical nowadays, as professional domain boundaries are blurring dynamically: Internet of Things (merging physical and digital engineering domains), biotechnology, genetic engineering, and others are just a few examples of global changes underway that academic education must quickly adapt to.


We believe that implementing [the] MERge pedagogical model in the academy and focusing on teaching meta-professions in [a] cross-domain or domain-agnostic manner can bring significant advantages to graduates. They will be more efficient in any professional domain and capable of adapting to changes happening in the job market.



[1] Schoen, D. The Reflective Practitioner How Professionals Think In Action.  Basic Books, 1984.

[2] Hazzan, O. The MERge pedagogical model for undergraduate science and engineering education, Tomorrow's Professor Postings, 1434, Stanford University, October 2015

[3] Hazzan, O. and Dubinsky, Y.  Develop by Your Education Goals: Concept and Technique

[4] Jablokow, K.W. et al. Creativity, Innovation, and Change (course), Pennsylvania State University

[5] Murphey, T.D.  Everything is the Same: Modeling Engineered Systems (course), Northwestern University

[6] Dunn, K. Experimentation for Improvement (course), McMaster University

[7] Severance, C. Internet History, Technology, and Security (course), University of Michigan

[8] Genesereth, M. Introduction to Logic (course), Stanford University

[9] Leitgeb, H. and Hartmann, S. Introduction to Mathematical Philosophy (course), Ludwig-Maximilians-Universität München

[10] Devlin, K. Introduction to Mathematical Thinking (course), Stanford University

[11] Page, S. Model Thinking (course), University of Michigan

[12] Osborne, J. and Sedlacek, Q. Reading to Learn in Science (course), Stanford University

[13] Metrick, A. and Geithner, T.F. The Global Financial Crisis (course), Yale University

[14] Burnett, B. et al. The Science of Decision Making (course), Worldview Stanford, Stanford University

[15] Jackson, M.O., Shoham Y. and Leyton-Brown K., Game Theory (course), Stanford University

[16] Levin, D. Teaching Character and Creating Positive Classrooms (course), Relay Graduate School of Education

[17] Klucharev, V. Introduction to Neuroeconomics: How the Brain Makes Decisions (course), Higher School of Economics, Moscow

[18] Sinnott-Armstrong, W. and Neta, R. Think Again: How to Reason and Argue (course), Duke University

[19] Barbara, O. and Sejnowski, T.  Learning How to Learn: Powerful Mental Tools to Help You Master Tough Subjects (course), University of California, San Diego