Constructivism Today: How Should Students Learn?

nürnberger-trichterOur schools: Same old, same old

The most commonly voiced out critique against traditional rote learning is that it doesn’t deliver what it promises, this is that acquired knowledge fades fast and students start to forget mental content shortly after their exams. In this model, learning serves to achieve a good grade by internalising syllabus material as fast as possible, forgetting it as fast as possible and to move on to the next short-term goal. But even if students would fully remember the knowledge that they were presented in class, they could do very little with it – perhaps with the exception of impressing their peers in TV game shows and quizzes that test for the recall of isolated facts. Knowledge acquired by rote learning is internalised passively. It is neither actively acquired by the learner, which would entail intrinsic motivation, nor applied within a real-world (and not merely academic-hypothetical) context.

Above: Traditional German illustration of the ‘Nürnberger Trichter’ (‘The Funnel of Nürnberg’). The writing says ‘First dumb and stupid, now clever as Goethe, all of which has been achieved by the funnel’s power’.

How about intelligence?

According to Sternberg’s Triarchic Theory of Intelligence (Sternberg, 1985), the mere recall and modulation of fixed content circumvents various forms of intelligence, which are analytic intelligence (the ability to apply new knowledge to solve real-world problems), creative intelligence (the ability to create innovative and novel ways to solve problems and to design systems) and practical intelligence (the ability to make internal changes to adapt to new environmental conditions). As a result, students in traditional schools learn hard but remain incompetent. What is measured in most schools and colleges are not aspects of intelligence, but the individual ability to endure stress and anxiety, the level of supportive upbringing provided by parents and the ability to regurgitate and parrot the mental content set out by the school’s curricula. Within such settings, students are assessed as solitary actors in a rather mechanical manner, illustrated fittingly by the ‘Nürnberger Trichter’.

new jobs

Above: Courses advertised at Udacity. The digital economy requires proactive, self-directed and intrinsically motivated learners. From the perspective of emerging technologies, Sternberg’s Triarchic Theory of Intelligence celebrates a comeback.

It is not about how long we remember what we have learned

The primary goals of knowledge acquisition, however, are neither the long-term recall of mental content nor to become a tough solitary learner. Actual cognitive and metacognitive performance is demonstrated by students being able to create concepts and tools to solve problems, to design systems that help people improve their lives, to develop positive social relations with others and to strengthen their autonomy. These educational outcomes are rarely assessed in most institutions of Higher Learning but they are more commonly found in elite education. Elite learners know how to contextualise newly created concepts (such as e.g., in information technology, social sciences or engineering) and they are aware of underlying historical and cultural conditions that scaffold local social development.

helvetas

Above: Managing comprehensive project administration and supervision based on modern research and sustainable local development (picture: biodiversity project in Haiti by Helvetas, 2017)

As for most movements, constructivism has been developed by many contributors, notably by their founders Jean Piaget and Lev Vygotsky. Piaget’s approach can be described as socio-interactional constructivism with emphasis on the individual learner, whereby Vygotsky’s approach can be described as a cultural-historical and activity-based constructivism with emphasis on the social scaffolding of learning via a ‘Zone of Proximal Development’ (Vianna & Stetsenko, 2006). Both approaches share the assumption that knowledge and the meaning of knowledge are actively constructed in the learner’s mind, that learning evolves contextually and is facilitated by social interaction. The mind is not perceived as a passive container to accommodate fixed sets information and limited cognitive processing within the boundaries of these sets. Piaget was grounded in the biological imperative, set out by Darwin, of a child’s adaptation to the environment. Vygotsky, following Marxist philosophy, focussed on the collaborative and transformative nature of learning. His approach remains highly relevant in today’s digital economy and media society which is characterised by the omnipresence of collaborating teams, complex multi-layered project development, intelligent knowledge management and highly integrated network groups. Curiously enough, it is these cooperative competencies of 21st-century working environments that are barely taught, if at all, at schools.

As for most movements, constructivism has been developed by many contributors, notably by their founders Jean Piaget and Lev Vygotsky. Piaget’s approach can be described as socio-interactional constructivism with emphasis on the individual learner, whereby Vygotsky’s approach can be described as a cultural-historical and activity-based constructivism with emphasis on the social scaffolding of learning via a ‘Zone of Proximal Development’ (Vianna & Stetsenko, 2006). Both approaches share the assumption that knowledge and the meaning of knowledge are actively constructed in the learner’s mind, that learning evolves contextually and is facilitated by social interaction. The mind is not perceived as a passive container to accommodate fixed sets information and limited cognitive processing within the boundaries of these sets. Piaget was grounded in the biological imperative, set out by Darwin, of a child’s adaptation to the environment. Vygotsky, following Marxist philosophy, focussed on the collaborative and transformative nature of learning. His approach remains highly relevant in today’s digital economy and media society which is characterised by the omnipresence of collaborating teams, complex multi-layered project development, intelligent knowledge management and highly integrated network groups. Curiously enough, it is these cooperative competencies of 21st-century working environments that are barely taught, if at all, at schools.

How Design Thinking extends Constructivism

Although it is correct that context, learners’ self-regulation and social scaffolding play a central role in active learning, the success of achieving learning outcomes depends largely on achieving mastery in the construction, application and evaluation of cultural tools. In design education, tools are commonly known in association with software- and hardware tools (from silk-screens to 3D printers and visualisation software), but also as concept maps and design theories, such as ergonomics, human-centered design and social design.

Broadly defined, cultural tools are instruments of mind that encompass concepts, strategies, information collection and processing methodologies, culturally-mediated reflective and communicative practices as well as methods to relate inductive-empirical and deductive-theoretical inferences. Cultural tools are the means by which our lifeworld is designed and mediated. Cultural tools empower students to connect ideas with facts, to minimise the margin of error of empirical tools and to maximise the validity and relevance of theoretical concepts. Without the mastery of effective tools, teamwork and context awareness do not yield productive outcomes by themselves.

Design thinking is closely related to Problem-based Learning (PBL) as it consists of a series of logical steps to design products and services. These shared steps are (1) group setting, setting up a team, (2) problem definition and cooperative reframing of the problem if necessary, (3) the review of prior knowledge and hypothesis generation (for explaining phenomena) or setting goals and expectations (for creating designs and implementing interventions), (4) the identification of learning issues and gaps of knowledge (5) going through reiterative cycles of research and research review (inclusive of experimentation and creative exploration), (6) concluding solutions development, (7) final outcome presentation and (8) post-project assessment by the entire team. PBL, as well as Design Thinking, are grounded in procedural inquiry and follow best practices of empirical research. Solutions are developed in logical stages by a team and they are not arbitrarily assumed by a solipsistic learner following an elusive ‘model answer’ or ‘model solution’.

Tesla

Above: Modern production facilities like here at Tesla are a good example for the need of skilled and competent workers that can solve complex problems, such as to program and manage robots or track and diagnose anomalies within automated production processes.

On the point of mastering cultural tools, Howard Barrows noted that PBL has one root in the apprenticeship method whose roots go back to the dawn of history (Wee Keng Neo & Kek Yin Chyn, 2002) where learning by doing emerged within an intergenerational culture of mastery. Today, mastery is rooted in science, also referred to as learning science (Bransford, 2000) shifting the educational focus on the mastery of scientific methods in support of new and innovative ideas.

Another argument for a procedural approach to future education is that without explicit awareness of the in situ implementation of knowledge, corresponding responsibilities cannot be assigned in a meaningful manner. As we live in a highly complex and interconnected world where responsibilities dilute across chains of institutions and businesses, a central theme in Badura’s recent work on moral disengagement (Bandura, 2016), the need to design systems of responsibility and accountability reinforces the call for fundamental educational reforms. If students are not taught on how to build a better world at an early age, how can anyone expect sensible societal progress?

References

Bandura, A. (2016). Moral disengagement: How people do harm and live with themselves. New York: Worth Publishers, Macmillan Learning.

Bransford, J. (2000). How people learn: Brain, mind, experience, and school. Washington, D.C: National Academy Press.

Sternberg, R. J. (1985). Beyond IQ: A Triarchic Theory of Intelligence. Cambridge: Cambridge University Press.

Vivanna, E. & Stetsenko, A. (2006). Embracing History through Transforming It: Contrasting Pigetean versus Vygotskian (Activity) Theories of Learning and Development to Expand Constructivism within a Dialectical View of History. Theory of Psychology, Sage Publications.

Wee Keng Neo, L. & Kek Yin Chyn, M. (2002). Authentic problem-based learning: Rewriting business education. Singapore: Pearson Malaysia.

Why it is Time to Retire Bloom’s Taxonomy

china

Picture above: Exam among Chinese Students (Source: Tomo News)

“You cannot teach today the same way you did yesterday to prepare students for tomorrow. ” John Dewey

1. Historical Credit and Positioning

Bloom’s Taxonomy of Learning has reigned as one of the most influential pedagogical concepts for the design of school curricula until today. Formulated by Benjamin Bloom and colleagues in the mid-50s (Bloom et al., 1956), the taxonomy attempted to break away from behaviorist theories as well as learning via remembering (rote learning) by promoting higher-order thinking skills, such as analyzing, synthesizing and evaluating concepts. Taking a more holistic approach, the taxonomy includes the cognitive- (knowledge-based), affective- (emotive-based) and psychomotor (action-based) domain which explains its intuitive appeal to many teachers. We do not only learn with our heads but also by our actions and emotional experiences that reinforce cognitive processes and give them meaning.

In 2001, Anderson & Krathwohl (2001) published a revised edition of Bloom’s Taxonomy, suggesting that in the cognitive domain, creation appears as a higher-order process as compared to evaluation (ISU, 2017).

2. Limitation of Bloom’s Taxonomy

The most commonly voiced out critique to the taxonomy is that thinking does not operate within hierarchies, but that cognition and affect are neurologically and phenomenologically distributed processes that can assume a plethora of possible configurations. Additional reasons that cast doubt on the usefulness of Bloom’s taxonomy as a pedagogical concept shall be outlined in the following.

2.1 Lack of Scientific Validity

Currently, Bloom’s Taxonomy is more than 60 years old and it had been developed before extensive empirical research into cognition, metacognition and motivation were conducted. As such, the taxonomy’s main categories (Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation) are not supported by empirical research on learning, be it as a category or as a category within a hierarchy ranging from lower to higher-order thinking skills.

bloom-large

The only terms of Bloom’s taxonomy that are validated by research are factual-conceptual knowledge (described in modern pedagogy as ‘prior knowledge’) as well as procedural- and metacognitive knowledge. In psychology metacognition is further differentiated into metacognitive knowledge, metacognitive regulation and metacognitive experiences (Efklides, 2006; Schraw & Moshman, 1995; Schraw et al., 2006) and it can appear in the form of individual metacognition, the reflective thinking related to mental content ‘due to me’, or social metacognition, the reflective thinking related to thinking about mental content ‘due to others’ (Briñol & DeMarree, 2012; Kim et al., 2013).

The question remains if lower- and higher-order thinking skills exist as such. A closer look questions this assumption. Some examples: The hierarchy of Bloom’s cognitive domains is broken in the case of (a) a problem-solving scenario where a solid comprehension of basic facts may outweigh an evaluation that is based on biased perceptions (besides, where does comprehension stop and where does evaluation start since both processes work reciprocally) or (b), where a concept is tested for the robustness of its causal and conditional relations (analysis) in order to obtain approval (final evaluation). In such case, analysis and evaluation are interdependent and one cannot be confirmed without acknowledging changes in the other.

If e.g., a situation is evaluated as problematic then this stimulates analysis on how to deal with it, entailing a subsequent evaluation of potential solutions. Even if a final solution is decided this leads to a retrospective analysis and check on the efficacy of the applied solution – and so on and so forth. There is, strictly speaking,  neither a clear-cut hierarchy nor sequence of cognitive processes since we are dealing with interactive, mutually dependent processes: no analysis without prior evaluation, no evaluation without prior analysis.

Creation, to comment on the revised taxonomy (Anderson et al., 2001), does also not necessarily constitute a higher order domain when underlying data analysis and conclusions of a project are faulty. Without proper research, creations remain guesses and assumptions. More than often, people try to promote their pet ideas and care little about thorough procedural solutions development. How do we determine the value of creation? In research, deductive and inductive reasoning are interdependent: we cannot blindly analyze things without an initial sense of intuitive comprehension (such as notions of purpose) and we cannot comprehend things without some sort of prior evaluation. Inductive-empirical and deductive-theoretical inferences relate reciprocally. The more tightly deductive and inductive inferences relate, the smaller the margin for error in research and development. For this reason, assuming a static hierarchy of domains like in Bloom’s Taxonomy is not helpful.

2.2 Lack of an Epistemological Base

Knowledge creation and relating thinking skills do not exist as a priori phenomena, but they are evoked and engaged by people. Knowledge is a foremost social construct while learning is facilitated by social processes (Bandura 2001, 2006). In this light, Bloom’s Taxonomy does not take into consideration the social relation of persons in the creation of knowledge. This includes crucial aspects such as the motivation to acquire knowledge, reiterative and diverse cycles of research, dynamics of open inquiry, the validation of related arguments or the ongoing refinement of concepts within teams. Bloom’s Taxonomy tells us nothing about the role that learners play in knowledge acquisition and creation, including a learner’s intellectual values, the psychological effects of learning experiences, individual differences in cognitive processing, or the communicative processes involved in research and development. Bloom’s Taxonomy does not explain how people collaboratively create, manage and modify knowledge.

Epistemological questions ask things like ‘How do we know that we know?’ or ‘How do we make sure that our knowledge is valid, reliable and relevant?’ The answers to such complex, but critical questions cannot be concluded by attributing general categories (e.g., ‘to analyze’, ‘to synthesize’), but via open deliberation among multiple learners.  Assessment cannot be based on ticking boxes of which cognitive domains have been covered by a student, but by assessing the quality of underlying reasoning.

2.3. Practical Disadvantages and Methodological Flaws

Other potential disadvantages of applying the taxonomy in curricula are (a) the lowering of expectations for higher-level deliberation and reasoning among students by ascribing complex, interrelated processes to simple domain identifiers (b) creating a false notion of ‘higher order’ versus ‘lower order’ outcomes. The taxonomy misleads educators to apply these perceived categories in separation, hampering a natural flow of logical reasoning such as in group discussions and (c) the identification of cognitive processes within an individual learner makes little sense. Instead, a student project can be structured according to logical stages, such as problem identification, problem reframing, identification of learning issues, self-directed research, research review, solutions development, solutions presentation and team/ self- review.

Bloom listed specific ‘action verbs’ that he claims are identifiers for the main cognitive domains, but it is easy to demonstrate that such simple correlations using ‘action verbs’ are misleading. For example, if we take Bloom’s domain of ‘evaluation’ in isolation and only look at action verbs, a student may e.g., ‘compare’ facts without involving analysis, ‘describe’ a phenomenon without explaining its underlying causality and context, or ‘justify’ an argument without giving valid reasons to why is should be believed. It is the power of interconnected, reflected and articulated reasons that drive cognition, not the mere presence of verbs.

3. Conclusion

Educators are looking for evidence-based strategies to enhance their students’ learning. Since Bloom’s Taxonomy is neither based on scientific findings nor offers an epistemological base that explains how knowledge is specifically created and modified within a socio-cultural context, it provides little reason to why it should be employed in educational settings.

The advantages of a constructivist approach, by contrast, are obvious: what matters is not the categorical identification of cognitive processes for the sake of ticking boxes. What matters is to determine how cognitive constructs have been assembled by the learner, which reasons and motivations went into the formulation of mental content and how knowledge-creation ties into larger meaningful frameworks such as cultural identity, human relationships, consensus finding, policy making, or the advancement of local and global communities.

In closing, Bloom’s Taxonomy, despite its historical merits, should be retired as an educational philosophy on the following grounds:

  • The taxonomy is not empirically validated
  • The taxonomy focusses on abstract cognitive domains rather than on learners. The taxonomy is not learner-centered and does not answer questions regarding a learner’s autonomy, competence and social relatedness (Deci & Ryan, 2012), all critical to learning.
  • Real-life contexts and their relevance for knowledge creation are not part of Bloom’s taxonomy
  • The taxonomy does not take into consideration the meaning that knowledge creates for a learner or a community of cooperating learners
  • The role of prior knowledge is not operationalized from an epistemological perspective
  • Motivation, the key component to learning, is not part of the concept
  • Individual differences in learning styles and attitudes remain unaccounted for
  • The taxonomy provides no sensible, specific criteria for assessment, such as evaluating students in their role as team members, researchers, and problem-solvers
  • Thinking processes are not based on strict sequences or hierarchies. Depending on the kind of problem at hand and its complexity, learners structure affective, cognitive and metacognitive processes accordingly
  • The obsession with individual cognitive skills and processes is often exercised at the expense of personal development, social skills, communication skills and the development of cooperative behavior

In all fairness, we have to consider that Bloom lived in a time and culture that celebrated uncompromised individualism. Bloom still shared the assumption of solipsistic learners whose learning can be objectively measured by a clear-cut hierarchical taxonomy. Empirically validated theories of social learning and studies investigating intrinsic versus extrinsic motivation or cognitive construction had not yet appeared on the horizon when Bloom worked on his taxonomy.

 

References

Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman.

Bloom, B. S.; Engelhart, M. D.; Furst, E. J.; Hill, W. H.; Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company.

Bandura, A. (2001). Social Cognitive Theory: An Agentic Perspective. Annual Review Of Psychology, 52(1), 1

Bandura, A. (2006). Toward a Psychology of Human Agency. Perspectives on Psychological Science, (2). 164.

Briñol, P., & DeMarree, K. G. (2012). Social metacognition. New York, NY: Psychology Press.

Deci, E. L., & Ryan, R. M. (2012). Motivation, personality, and development within embedded social contexts: An overview of self-determination theory. In R. M. Ryan (Ed.), Oxford handbook of human motivation (pp. 85-107). Oxford, UK: Oxford University Press. doi: 10.1093/oxfordhb/9780195399820.001.0001

Efklides, A. (2006). Metacognition and affect: What can metacognitive experiences tell us about the learning process? Educational Research Review, 13-14. doi:10.1016/j.edurev.2005.11.001

Iowa State University (2017). Revised Bloom’s Taxonomy. Retrieved from: http://www.celt.iastate.edu/teaching/effective-teaching-practices/revised-blooms-taxonomy

Kim, Y. R., Park, M. S., Moore, T. J., & Varma, S. (2013). Multiple levels of metacognition and their elicitation through complex problem-solving tasks. The Journal Of Mathematical Behavior, 32(3), 377-396. doi:10.1016/j.jmathb.2013.04.002

Schraw, G., & Moshman, D. (1995). Metacognitive Theories. Educational Psychology Review, (4). 351.

Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting Self-Regulation in Science Education: Metacognition as Part of a Broader Perspective on Learning. Research In Science Education, 36(1-2), 111-139.