How the Key-Challenge in Digital Education Is Not Primarily About Technology, but the Digital Scaffolding of Social Spaces and Self

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Nao Picture Credit: Flickr/Stanford Center for Internet and Society, CC BY-NC-SA

Looking for a deeper understanding

The effect of digital media on learning is the pervasive power behind the current revolution in education. Ranging from Open Educational Resources (OER) to Blended Learning Models, media profoundly transform key topics of education. Subjects such as media literacy, media education, media pedagogy, the acquisition of digital competences, the digital divide, media socialization, and e-learning, among many others, dominate educational discourse worldwide. But can we find a deeper and more coherent philosophical understanding of the historical paradigm shift?

The digital-pedagogical imperative

Compared to traditional classrooms equipped with a chalkboard, printed geographical maps and textbooks, digital media allow for new ways of representation, interaction, knowledge-creation, and social situatedness. Teachers keep fading into the background as the sole source of information, and start assuming the role of information managers: it is not relevant what has been taught, what content got covered in class, but what has been learned and by whom. Digital media extend forms of expression, they encourage the active (re)production of knowledge and provide new ways of conceptualizing the Self and its positioning within the social world.

Soft-skills suddenly become more important than uninformed leadership as social networks provide support for personal growth and professional opportunities. Perhaps even more profoundly, digital culture challenges standardized assessments (the PISA studies as an example), which are still based on the assumption of homogenous learner populations, given a standard distribution. Although such assumptions of homogeneity may have been valid to some degree until the first half of the 20th century, educators today experience that this is not the case any more in diversifying pluralistic societies.

Still, few education providers realize how learning outcomes, learning methods as well as the roles of teachers and students become highly interconnected within the cultural paradigm shift. More complex learning outcomes, in particular in the field of personalized and collaborative learning (‚active learning‘), imply new roles of students and instructors. Student turn into junior researchers, lecturers turn into learning consultants and the school itself turns into a community of interconnected learners. Things couldn’t be more different from the past. Innovative methods of learning inevitably produce new types of desired outcomes, such as the development of soft-skills and context-independent problem-solving competencies, also known as transfer skills.

Once we realize the fundamental phenomenological, socio-cultural and economic shifts of the digital age, it becomes more apparent how media, as a merely technological construct, is of lesser interest to educators. The features of digital equipment are of superficial value. Of actual interest are the effects of media on learning processes, classroom interactions and the construction of learning environments, such as digital school development.  We may label this focus as the digital-pedagogical imperative. However, differently from traditional media such as books or blackboards, digital media require a minimum of technical skills and knowledge for their use. And prior to use, students require basic knowledge about the functionality of digital media inclusive of their social effects (such as e.g., decision-making processes via algorithms, machine learning affecting social actors or issues regarding data security). Hence, the call for digital competencies across the board of educational institutions.

Towards a social, rather than a technical perspective in media-supported education: The concept of hybrid space-time

For the field of education, the shift from a technology-centered towards a socially-centered perspective renders helpful for a number of reasons: Firstly, we keep the eyes on the prize, which is our educational mission and vision, regardless of technological trends. The question still remains how technology can serve achieving prime educational directives, such as the democratic development of society, to prepare school leavers adequately for a highly dynamic working environment and a journey of lifelong learning. Secondly, by taking a more social view, we are able to develop strategies to empower learners by structuring digital environments more systematically, determined by hybrid (socio-digital) space and time. The term shall be explained.

The central achievement of the network society, besides creating ‘spaces of flow’ (Castells, 1998) lies in connecting previously un- or barely related social spaces, to use Uri Bronfenbrenner Ecological Systems Theory, while widening our phenomenological consciousness technologically, to quote David Chalmer’s beautiful metaphor of the ‘Extended Mind’ (Chalmers, 1998). Chalmers promotes the idea that media, such as, e.g., smartphones, have already begun to function as an extension to our mind, allowing us to navigate and manage an increasingly complex world. Social perception, social connectedness, epistemology and mental spaces of actors have become increasingly interwoven with and by digital media.

The interconnectedness of digital spaces, however, requires the scaffolding of appropriate social norms and learning opportunities. There is little point of creating apps and platforms if people don’t use them effortlessly and productively in their lives. Each sphere of Bronfenbrenner’s Micro-, Meso-, Exo- and Macrosystems (Bronfenbrenner, 1979) calls for corresponding ‚digital scaffolding‘ in addition to face-to-face interactions. Analogous to Vygotsky’s concept of a Zone of Proximal Development, or ZPD (Vygotsky, 1980), each of Bronfenbrenner’s systems should support social actors with ample opportunities for navigating, developing, transforming, amending and reforming them and allowing for the creation of new spaces if necessary. Some examples of digital social spaces are e.g., learning management systems (LMS) for schools and colleges, professional social online networks or layers of E-Government. The development is still in its infant stages.

Digital structures as a means of social interaction emerge superimposed onto Bronfenbrenner’s system descriptions, but may not be congruent in cases where networks cross traditional boundaries, be it to link professions, cultures or government agencies. Digital networks serve social actors to navigate and negotiate their multi-facetted biographical learning journeys across social spaces, closing the conceptual gap between biographical timelines and digitally enabled social spaces. In an ideal case, as we progress along our biographical timeline, we learn how to widen and deepen our social systems – a process that is increasingly facilitated via digital media. Scaffolds can be defined as assisting concepts that help us grow into new social systems.

Honoring the theorists mentioned before, we may call these new learning scaffolds Bronfenbrenner-Chalmers-Vygotsky (BCV) spaces: Virtual spaces transcend physical spaces by extending the perspectives of social actors. The purpose of these spaces is to widen their options for scientific investigation, social networking, negotiation and co-creation.

Problem-solving requires s higher level of complexity than the problem at hand. On a global scale, there are no more easy problems.

Beyond academic interests, the latter idea of responsible co-creation has severe economic consequences, in particular on a limited planetary scale where the ecological and social costs of production and consumption have reached their critical limits and cannot be outsourced, or passed on, anymore. The educational lesson states that solving global and regional problems, especially those created by single-minded interest groups, can only be addressed successfully by higher-qualified, multi-disciplinary  teams capable of managing the complexity of issues at hand.

The underlying hypothesis could be formulated as follows: Problem-solving strategies demand a higher level of cognitive and metacognitive complexity than the grounds (causes and reasons) that have created the problem in the first place. Understanding a problems implies reframing it within its boundary conditions. Following the argument of rational problem solving (and sharing the assumption that this is what we are aiming for), the digital scaffolding of BVC-spaces evolves as a key-competence for future problem-solving. This means that in order to solve high-complexity problems, social actors require the skill to create hybrid spaces to accommodate their research, management, modes of interaction and policy development.

BVC

Illustration above (by the author): Hybrid space-time as the new medium in which education evolves – autobiographical trajectories, dynamic networks across social spaces and phenomenologies merge into multidimensional constructs

To this extent, it appears more sensible to conceptualize digital media within such an integrated, multidimensional framework, rather than sticking with a relatively simplified viewpoint that focuses reductively on technology and its apparent performative advantages or disadvantages, (e.g., the TPACK model). Naive and counterproductive-conservative approaches, such as Hattie’s promotion of ‘what works in the classroom’ (Hattie, 2010), advocate the regression to lower-complexity teacher-centered models. Besides paying homage to a bygone era of instructionalism, such approaches are bound to fail culturally. Traditional teachers are currently dethroned as sole providers of authoritative content by digital natives who migrate into parallel learning universes consisting of YouTube Videos, social networks and improvised peer instruction. In order to bring teachers back more meaningfully into the classroom as learning guides and consultants, constructivist pedagogical approaches, based on the integration of digital media and an active learning paradigm (see Gagnon & Collay, 2006 for a practical introduction), appear to offer a  more future-oriented outlook.

As a practical example, we could look e.g., at interactive whiteboards as either a fancy instrument that cements teacher-centered instruction or as an opportunity to create reflected discourse in class; or we may look at highly immersive technology such as VR and raise pedagogical questions about the quality and sustainability of invoked learning processes. The answer to the justification and evaluation of technology lies in its application effects, but not technology per se. Technology is only as good as the purpose that it serves and the objectives that it achieves.

Beyond wishlists of digital competencies: Looking for the missing link to digital resource planning

Educational competency wishlists are easy to compile. What is still missing in research is the missing link between physical planning and the virtual construction of education. Multi-factorial and highly dynamic learning environments require a new language, a new syntax in order to plan, implement, validate, optimize, develop and predict the efficacy of Blended Learning Models (see Garrison & Vaughan, 2013; Picciano et al., 2014). One notoriously under-rated factor in this context is the limited human and financial resources of education providers versus the increasingly complex demands posed by the digitization of education. OER need not only be shared but created, modified to suit target groups, peer-reviewed and adapted to work smoothly within a plethora of Blended Learning Scenarios. The additional work for employing digital media requires substantial financial investment, staff and time: Staff needs to be trained and diversified in employing media-supported pedagogical strategies, more flexible financing procedures for procuring digital media need to be developed, parents need to be informed about best media literacy practices for their children, students need to adapt to take more responsibility for their learning – it is a long  list of resource-intense challenges.

Canvas Joana Kompa

Screenshots above (by the author): Learning Management Systems, like here in Canvas LMS, allow for the more productive connection between students and lecturers. They also allow for deeper, meta-cognitive insights into one’s own learning processes as well as creating a more learner-friendly, fluid academic environment.

In this light, collectively shared resources influence the quality and scope of digital education, in particular for serving heteronomous student populations and their demand for personalized learning. As the planning of shared resources cannot be conceptualized independently from pedagogical strategies anymore, their interconnectedness poses an entirely new challenge to social innovators.

The author is working as a scientific consultant for digital education at the Carl von Ossietzky Universität in Germany. She is one of the founders of the Medienfaktur and member of the workgroup ‚The Digital Competence Framework for Educators‘ (DigCompEdu) of the EU-Commission.

Literature

Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press.

Castells, M. (1998). The Information Age. Economy, Society and Culture. Oxford; Malden, MA: Blackwell.

Chalmers, D. & Clark, A. (1998). The Extended Mind. Analysis 58.1, January 1998, pp. 7–19 Retrieved from https://icds.uoregon.edu/wp-content/uploads/2014/06/Clark-and-Chalmers-The-Extended-Mind.pdf

Gagnon, G. W., & Collay, M. (2006). Constructivist learning design: Key questions for teaching to standards. Thousand Oaks, Calif: Corwin Press.

Garrison, D. R., & Vaughan, N. D. (2013). Blended learning in higher education: Framework, principles, and guidelines. San Francisco, Calif: Jossey-Bass.

Hattie, J. (2010). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London : Routledge

Picciano, A. G., Dziuban, C., & Graham, C. R. (2014). Blended learning: Research perspectives, volume 2.

Vygotsky, L. S. (1980). Mind in- society: The development of higher psychological processes. Harvard University Press.

Why I left Facebook: A Letter to My Friends

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Dear Friends,

When I joined Facebook about eight years ago, the more tragic consequences of social networks had not arrived yet. There were no Russian trolls, there was no US voter manipulation, rigging the system for a complete madman, no Cambridge Analytica, no support of the Rohingya genocide in Myanmar and no tolerating of other hate-groups since political echo chambers had not visibly arrived yet. Mark Zuckerberg never realized how he had been living in cyber-paradise for all these years until he got thrown out of it. Like us.

Facebook was, at least in its early stages, still a cosy global village, to use Marshall McLuhan’s metaphor, that kept me connected to friends from all over the world. If you ever watched an episode of ‚Friends‘, to spin a metaphor, Facebook would be the sofa arrangement in the middle of the living room. Functionally, it was my international couch. To give up these long-grown connections is a sad process, in particular since my biography has evolved in a fairly cosmopolitan manner over decades (Germany-Singapore-Shanghai-Bangkok). So why leave the comfort zone?

We get sold out – the user is the product. Facebook’s latest scandals go beyond a series of unfortunate events. For starters, Facebook is harvesting user data to generate income for advertising which, by itself, would bother me a little as advertising flyers ending up in my letterbox. A necessary inconvenience. What does bother me, however, is the use of my photos and data to e.g., create 3D videos on ‚Friends Day‘ – without my explicit consent. But I forgot… this consent was already given by me implicitly by agreeing to the Terms and Conditions. Whoever reads those? Sloppy me! Likewise, my personal data is used to create a consumer profile. Which other profiles get distilled, I wouldn’t know. In terms of available data from over two billion users, Facebook dwarfs the NSA.

We get catalogued and profiled for harvesting our data. The actual profiling algorithms, or an adequate explanation of how they work and what they do, remain hidden from us. They are trade secrets and company property. After all, Facebook is a private company and not responsible for public welfare or transparency. We are kept in a virtual pink bubble-world of happy friends. The mathematical truth is, similar to an ant colony, that the single user is expendable. It is the network effect of more than two billion active users that renders Facebook valuable. Similar to Gmail, it could be argued that user data for profiling gets anonymized. Still, I am not willing to give my consent to an unregulated online economy any longer.

Silicon Valley giants evade contributing to society. I am not ok with an economic model built on the surveillance of consumers, to use Shoshana Zuboff’s argument. The long-term effect of online monetarization via dominating players such as Facebook and other digital overlords lead to the unregulated monopolization of capital, the convergence of trend-setting ideas and centralisation of technology in the hands of a few. And yes, Facebook, like other Silicon Valley mega-players, hardly pays taxes to contribute to society. Its do-gooder PR is all smokescreen.

Social media is addictive and, quite frankly, a waste of time. The second reason for abandoning Facebook is the conditioning of social behaviour. How much time do I waste these days on Facebook or Netflix? I was talking to friends last Christmas and we realized how glad we were to have spent our childhood and youth offline. Digital media can be great, but only when balanced with productive offline life. Facebook has been designed to keep users hooked, not to encourage significant online breaks.

I doubt that I would have developed any of my knowledge, skills and competencies if I have had access to electronic drugs during adolescence. If Mozart was born today, he would have probably posted some impressive videos on YouTube, collecting more than a million likes. There would certainly be hype, but there wouldn’t be a Köchel catalogue. I much doubt the Beatles or Bowie would have succeeded either, perhaps as trendsetters in obscure Online indie-radio channels. Given today’s cultural climate, Freddie Mercury would have received a never-ending stream of homophobic hate mails. Well, long live the Bohemian Rhapsody and long live our Starman.

We neglect our analogue development. Developing one’s full potential requires self-discipline (the exact opposite of instant gratification) and focus (the opposite of consuming hundreds of images and snippets of information every day). To this extent, Facebook belongs to the category of distracting tech. If I was close to retirement or had all day to follow nothing but my hobbies, Facebook would be a great way to connect. If Facebook ever gets regulated, we may talk again.

We become vulnerable. The third reason is, of course, privacy. Try to google for ‘Hacking Facebook accounts’ and you get the drift. Software that offered ‘victim-management’ popped up in an instant. One does not have to dive into the depths of darknet to find a plethora of such options. I was just plain lucky to never get hacked but many close friends have. Still, I told myself ‘I never angered anybody personally in my life and I am not a person of interest, such as public figures, so why to bother?’. This was, waking myself up here, exactly the wrong assumption that gets users into trouble.

In 2019, we cannot be naïve (‘This will never happen to me’). Anybody who speaks out for emancipatory values and open society, especially intellectuals, social reformers, scientists, journalists and public speakers, is already a potential target for right-wing hackers and haters. As a consequence, there is no Facebook, no Instagram or Tumblr for me. I kept Twitter to follow some News channels, although I do not use it much beyond announcing a new Blog entry.

The Online God sees everything and won’t ever forget your sins. Everything we ever post can be used against us. Snippets of text (and video, deep fakes will become more accurate fairly soon) can be easily quoted out of context to suit somebody else’s agenda. Private photographs are most easily exploited. Once we speak up openly, we become a target on troll radar or a malicious pattern-searching algorithm – if not our data, then our metadata and digital breadcrumbs. I am taking precautions to not end up like some German politicians and celebrities this week, who found their confidential data hacked and published on Twitter. Besides, why would people need to know my whereabouts in the first place? What I eat or my relationship status? Give me a break! Post-Facebook, you will meet me next, somewhere save (in Morrissey’s words) in Far Off Places.

We are prevented to re-invent ourselves freely. Facebook’s timeline can easily turn into a corset for anyone who changes their life, be it a young man who suddenly discovers that he is gay, a woman after a divorce, a successful manager who starts at a competitor, but still has his entire social circle on Facebook – in fact anyone who starts a new life and deserves a fresh start without historical baggage .

Facebook can destroy relationships. Facebook is prone to cause irritations and misunderstandings when it comes to personal relationships. One has to constantly monitor his or her online public persona, every photo, tag, comment and publication setting, which is by itself an exercise in self-censorship and virtual stress. Facebook-use renders us paranoid, touchy and puts us into a perpetual mode of social distrust. Virtual life can eat away real life, and I am not even talking about online mobbing.

Can it ever be useful to be on Facebook? In this Blog, I stand firmly for what I believe in and defend my principles. If I ever would enter politics, I might return to Facebook as a public persona in order to, how PR-experts coined the term, ‘control the narrative’. This would be a very different scenario where one has to be ready to fight online. In contemporary politics, it comes as part of the job description. Unfortunately, as numerous examples of German politicians, judges and journalists confirm, standing in the spotlight in Western countries may even entail murder threats to oneself and family. Progressive politicians such as Robert Reich, Bernie Sanders, Elizabeth Warren and countless others handle social media fairly well. Prerequisite for a Facebook representation would be if ever, a public mandate of representation. Facebook can be useful once your private life isn’t on it.

On the upside, good friends stay beyond Facebook. When we meet again in person, we will have stories to tell. There will be no more online-published previews of developments that suck the magic out of social events, allowing for genuine surprises and experience to accumulate. Offline life yields its merits. The quality of deep human connections supersedes the need for continuous online presence.         

I will dearly miss your originality, humour and wit. Stay in touch through secure channels.

Joana

(I left Facebook for good on 6.01.2019)

Postscriptum:  Efforts are underway to counter the centralisation of the web. If you are more of a geek, have a look at the latest works of Tim Berners-Lee, inventor of the World Wide Web https://www.fastcompany.com/90243936/exclusive-tim-berners-lee-tells-us-his-radical-new-plan-to-upend-the-world-wide-web   or Cambridge-based startup https://fetch.ai/Facebook has just announced to merge Facebook, WhatsApp and Instagram. Quod Erat Demonstrandum.

John Hattie: Invisible Learning, Visible Amateur Science

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The picture above: Many schools today are far more friendly and advanced as schools 40 years ago. Yet, in Hattie’s meta-meta study, schools of different decades are thrown into the same basket to search for statistical effect sizes of ‘what works best’. Picture Credit: Bundeszentrale für politische Bildung.

Introduction: Which factors? Measured how? For which purpose?

Let’s start with a joke about statistics:

Three statisticians went out hunting and came across a large deer. The first statistician fired, but missed, by a meter to the left. The second statistician fired, but also missed, by a meter to the right. The third statistician didn’t fire, but shouted in triumph, “On the average, we got it!”

Averaging effect sizes assumed by a normal distribution can be tricky business. When it comes to people (or deer for that matter), the average only exists as a social construct, an assumed variable that demonstrates reliable efficacy for most members of a given population. In Hattie’s case, however, things get more muddled up as we are dealing with not only different hunters that appear at different times of the day, but hunters who never knew what they were actually hunting for and who, after they randomly killed an unknown animal anyway, by mere chance, concluded a posteriori that they had definitely not killed a deer.

If this sounds like a strange story with loose ends at every stage (to the point of not making any sense), you are welcome to the work of John Hattie who is cited and celebrated, around the world, as a ground-breaking educational researcher. I have written this blog post to allow for a comprehensive online reference to counter Hattie’s omnipresence.

I would not go as far as to accuse Hattie of pseudoscience, as Pierre-Jérôme Bergeron did in his review of Hattie’s statistics, since Hattie does not believe in supernatural forces per se (but omnipotent effect-sizes nevertheless), but I do assess his work as unbelievably sloppy and amateurish science to the point of where he is misleading the public. Other valid comments, such as on Hattie’s flawed statistics, include reviews by Neil Brown or Robert Slavin. Scott Eacott (2017) wrote an illuminating piece on Hattie and the damaging influence of his work on Australian education. For Eacott, Hattie’s work promotes a one-size-fits-all, neo-Taylorist approach to school leadership.

When John Hattie published his book ‘Visible Learning’ in 2008, he intended to pragmatically find out ‘what works best in education’. His basic idea appeared logical: the more studies can be collected about the factors that foster success in learning, the closer science might get to the Holy Grail of education. Hattie had hoped to find universal variables that, once and for all, would scientifically indicate what works best for students to achieve academic success. There are, however, a number of critical conceptual issues that Hattie failed to address. These are listed in the following.

The conceptual issues, in a nutshell

1. Lack of a clearly defined, valid and reliable construct

Hattie never clearly defined the dependent variable, which is student achievement. Since most studies use grades, Hattie (probably) assumed that grades are the smallest common signifier and denominator of students success. Hattie never clarified his all-encompassing variable or how it was concluded. He never explained how the variable of student achievement was constructed (based on which empirical evidence?) and in which context. If he had employed proper scientific methodology, he would have needed to conduct an Exploratory Factor Analysis (EFA) and a Confirmatory Factor Analysis (CFA), leading up to proper Structural Equation Modeling (SEM). Hattie never did any of this. He never defined the dependent variable he actually pretends to measure and which his entire book rests upon. There is no deer to hunt. Hattie is following a wild guess. If Hattie had indeed bothered to clarify the main construct of his study, he would not have been able to throw all studies, regardless of origin, quality or composition, into the grinder of a meta-meta-analysis.

2. The lack of validity of meta-meta studies for educational (and any other) contexts

In ‘Visible Learning’, studies from the 70s and 80s are mixed together with studies from the 2000s. Seriously… can we blindly assume that no progress has been made in schools around the world for the past 40 years? Hattie disregards factors of pedagogical development and culture, public policy changes, historical and socio-economic circumstances. There is no scientific evidence on the validity of meta-meta studies

Another good example of how blind (and rather naïve) quantitative data can lead to misleading analysis is the success of Vietnam in the PISA Studies. John Jerrim wrote a detailed analysis on the topic. The underlying argument says that quantitative analysis can lead not only to wrong but grossly flaky results if contextual factors are not taken into consideration. In the case of the skewed PISA ranking for Vietnam, the flaw was the lack of a proper definition of the sampling population. Hattie is guilty of the same mistake.

Let’s assume for a moment, that Hattie’s could be right by averaging effect sizes of ‘what works’ and extracting the gospel of universal factors for promoting student achievement. If the silver bullets were indeed available, policies would be drawn out in an instant to spend budgets on ‘what works’ while schools would be told to scale back of what doesn’t. So if you happen to work at a school that is not average, according to the almighty meta-meta study, your luck has just run out. As an example, Problem-Based Learning (PBL) rates very low in Hattie’s universe of effect sizes, whereby in Medical Education, it has empirically proven to be significantly superior to traditional approaches and has been, for decades, firmly established as a State-of-the-Art pedagogy (see Wang et al., 2016; Sayyah et al., 2017; Zhang et al., 2015).

3. The limitations of effect size to inform pedagogy

Let’s assume that we compare apples with apples and oranges with oranges, in which case we can argue with effect sizes based on the average, e.g., the average size of a typical apple or orange, grown on a particular orchard. The average gives us, well, just the average. It does neither tell us the size of the smallest, nor of the biggest apples or oranges. Averages do not tell, how and why, the best and the worst students perform as they do.

But what is the basis of effects? Did students have 20 minutes for a test or were they given a full hour? Had a test been based on mere rote-learning or was it based on problem-solving and developing creative strategies? Has a school project been conducted as an individual task or as a group project? Was a lesson designed more in an instructional or more in a constructivist manner, or as a mix of both? And if yes, was this mix adequate and efficient to foster student learning? Hattie does not bother with such trivialities. In order to compare measurements, one has to ensure that the instruments to take those measurements are at least comparable.

For Hattie to claim that one educational practice is per se ‘good’ (= large effect size) or per se ‘bad’ (= small effect size) defies all foundations of pedagogy. Didactic context is not considered in his work. A validated psychological or motivational model is absent in ‘Visible Learning’ as well. In order to make sense of any effect size in the context of classrooms, the inclusion of pedagogical and didactic models is a condicio sine qua non. An effect without context does not make any sense.

Below: Effekt sizes according to Hattie

hattie effects

4. Hattie’s self-contradiction

In concluding his book, Hattie differentiates between surface knowledge, deep knowledge and conceptual knowledge, all of which are regarded as essential. Hattie concludes that 60-80% of all knowledge created by tests, evaluations etc., qualifies merely as surface knowledge. This means that only 20-40% of all knowledge taught by teachers, passes as deep- and conceptual knowledge. Besides making a fairly bold statement – if this was indeed true, then all of Hattie’s measures of effect sizes would be practically meaningless: Once teachers intend to achieve deep- and conceptual knowledge, they would change their teaching strategies based on Hattie’s recommendations. In this case, predictably, most effect sizes would change significantly (!). This dilemma reveals another flaw of his research: Hattie uses data ‘as is’ (which he later identifies as predominantly inefficient teaching) but uses the concluded effect-sizes from the same data sets to formulate a hierarchy of best practices. Things cannot be valid (efficient teaching) and invalid (inefficient teaching leading to surface learning) at the same time, can they?

Hattie’s cult following should not distract from the fact that Hattie has neither contributed to the development of modern pedagogy nor has he informed sensible school policy. If I was looking for an inspirational figure (or charismatic guru) with a superior grasp on the dilemma of educational systems, I would look for someone like Sir Ken Robinson, or would professionally study constructivist pedagogy. Not all of Hattie’s writing is misguided. To give a more balanced review, I would need to mention his sensible ideas on feedback etc., but then again, these topics have been researched far better by others.

5. It  isn’t statistics

School grades, the only data available in Hattie’s Visible Learning to measure academic achievement, are set as ordinal scales. In order to calculate effect sizes with Cohen’s d (the standardised difference between two means), one would need at least an interval scale. This is how Hattie’s calculations might be something, but it sure isn’t statistics.

Literature

Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. London: Routledge.

Scott Eacott (2017): School leadership and the cult of the guru: the neoTaylorism of Hattie, School Leadership & Management, DOI: 10.1080/13632434.2017.1327428

Zhang Y, Zhou L, Liu X, Liu L, Wu Y, Zhao Z, et al. (2015) The Effectiveness of the Problem-Based Learning Teaching Model for Use in Introductory Chinese Undergraduate Medical Courses: A Systematic Review and Meta-Analysis. PLoS ONE 10(3): e0120884. https://doi.org/10.1371/journal.pone.0120884

The TPCK Model of Technological Education: Explaining Everything, Achieving Little

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Digital media are becoming more common in classrooms around the world. The underlying pedagogy and design of curricula determine the benefits to students. Picture credit: Northeastern University

Introduction

The TPCK (also known as TPACK) Model of Technological Pedagogical Content Knowledge, originally proposed by Misrah and Koehler (Koehler & Mishra, 2005, 2008, Misrah & Koehler, 2006), has taken the academic world by storm by proposing a pragmatic and systematic theoretical grounding to assist teachers integrating content knowledge, pedagogy and technology.

The model was developed by the authors during a five-year cooperation with American K-12 teachers and the faculty of Michigan State University. Its theoretical foundation is based on Shulman’s concept of pedagogical content knowledge, in short PCK (Shulman, 1986, 1987). At the heart of PCK stands the intuitive argument that in order for teachers being able to render content knowledge (CK) accessible to their students, it is a prerequisite for them to transform content knowledge in a pedagogically sensible manner (P), integrating both expert content and pedagogical knowledge. To their credit, Misrah and Koehler pointed out that professional organizations such as the National Science Teachers Association (NSTA, 1999) and National Council for the Accreditation of Teacher Education (NCATE, 2001) acknowledged the value of PCK for teacher preparation and teacher professional development.

Description of the TPCK Model

The authors gradually extended Shulman’s model by the component of technology (T), translating it into what is known today as the TPCK (or TPACK) model. How does this model work?

The-TPACK-Technological-Pedagogical-and-Content-Knowledge-model-of-educational

Graphic: A visualisation of TPACK

Using Shulman’s metaphor of three overlapping circular domains, the model concludes a total of seven key areas of knowledge. Beyond the technological, pedagogical- and content knowledge (the body of expert knowledge about a subject matter), four new areas come into play:

Technology Knowledge (TK) is defined by the authors as the teacher’s ability to use and apply digital technology in the classroom.

Technological Content Knowledge (TCK) relates to how technology shapes and influences the representation of content. Recent examples would be, e.g., the examination of phenomena by statistics, predictive analytics or computer simulations, opening new ways of exploring the world that was not available prior to the advent of digital technology.

Technological Pedagogical Knowledge (TPK) is defined by Misrah and Koehler as the pedagogical knowledge about the educational benefit of digital tools. The authors cite as examples digital class record systems, online grading, discussion boards and chat rooms.

Technological Pedagogical Content Knowledge (TPCK), finally, as the cross-section of all areas ”… is the basis of good teaching with technology and requires an understanding of the representation of concepts using technologies; pedagogical techniques that use technologies in constructive ways to teach content; knowledge of what makes concepts difficult or easy to learn and how technology can help redress some of the problems that students face; knowledge of students’ prior knowledge and theories of epistemology; and knowledge of how technologies can be used to build on existing knowledge and to develop new epistemologies or strengthen old ones.” (2006, p. 1029.)

A Critical Reading of the Original Script

The original script contains two segments. The first describes the TPACK model, as outlined above, while the second segment was named ‘APPLYING THE TPCK FRAMEWORK TO PEDAGOGY’. This section advocates what is typically described in the literature as design thinking (Brown & Katz, 2009; Lietka et al., 2017; Plattner et al., 2016; Rowe, 1998). Mishra and Koehler define their approach explicitly as ‘learning technology by design’ (p. 1034).

In this application part, the authors forward some valid points that are typically conceptualized in constructivist pedagogy. These arguments consist of notions such as

  1. Technology changes fast, which is why teachers need to be able to update their skills, classroom content and mode of delivery continuously. This argument also entails (contradicting TPACK) that learners cannot primarily rely on content that is easily outdated, advocating the development of process skills to make appropriate content available and to relate facts with changing ideas and complex concepts.
  2. Learning happens in situated contexts, which is why digital technology needs to support the age of learners and their developmental needs, the type of school or a variety of individual learning styles. In design thinking, this notion is complemented by taking a user- or human-centred approach. This entails that pedagogy is not arbitrary as well.
  3. Competencies need to be developed: A mere wish-list of competencies does not tell teachers on how to achieve them. TPACK promises a structured approach to guide implementation.

On face value, the TPACK model appears intuitive in stating technology, content and pedagogy as the main drivers of education. Similarities to theories such as Maslow’s Hierarchy of Needs or Bloom’s Taxonomy arise in so far, as an additive modular framework is proposed to explain and guide the complex dynamics of learning processes. TPACK seems to intuitively answer to all potential combinations of its components by starting with simplified assumptions.

A closer view, however, reveals that TPACK rests on a number of problematic assumptions:

  • Isolated domains: The first assumption is that technology, pedagogical- and content knowledge exists in isolation and can be understood by relating them via distinct overlapping circles as a representation of empirical reality. The second reading of this first assumption is that technological, content- and pedagogical knowledge exists as an a priori construct, each type of knowledge separated from the learning processes of involved actors. Shulman’s original categorical distinction between pedagogical and content knowledge was not further questioned by Misrah and Koehler. Citing McEvan & Bull (1991) and Segall (2004), Archambault and Barnett (2010) pointed out that ‘content, in the form of scholarship, cannot exist without pedagogy, and (…) explanations of concepts are inherently pedagogical in nature.’ (p. 1657). To this extent, Shulman’s framework appears to unnecessarily complicate the reciprocal relationship between content knowledge construction and the corresponding metacognitive reflection (pedagogical knowledge) that accommodates and frames it. Adding technology to the equation does not solve the initial lack of construct validity.
  • The dominance of content knowledge: The second assumption is that content knowledge (CK) represents one of the main components of digital- or media-education and should be regarded as one of the main educational concerns. It is not elaborated and justified why and how that is. Modern pedagogy is concerned about the acquisition of competencies, personal resources and process skills, but not content per se. Content is framed by overarching concepts and therefore cannot be separated from discoursive epistemological access.
  • A Model without actors and goals in mind: The third assumption is that the placing of abstracted thematic blocks (T, P and CK) is the most appropriate manner to plan learning and teaching activities with and about digital media. By excluding the actors of learning processes from the model (teachers, facilitators, students or the school administration), TPACK is not developed as a student-centred framework, which should be a grave concern for any educator.

The Design Thinking Conundrum

The TPACK application section focusses on design thinking which is, by definition and its human (learner)-centered approach, constructivist in nature. Typical issues of design thinking do not deal with content dissemination, but problem-solving, the development of innovative and novel solutions to challenges, the creation of new knowledge and ideas. Design thinking is about facilitating testable hypotheses as well as developing and improving ways of communication and group collaboration. There appears to be an internal contradiction and gap in the approach suggested by Misrah and Koehler; this is to build on a teacher-centered model based on content dissemination on one hand and to promote design thinking, which is based on active self-directed learning, on the other. There is neither a clear explanation given nor a discussion facilitated by this significantly different and incompatible choice of philosophies. The elephant standing in the room is the role of constructivist pedagogy. Since the TPACK model had been originally developed in collaboration with K-12 teachers, it is likely that teacher-centred pedagogy was assumed as the default model whereby design thinking was added on as a technological-compatible, convenient afterthought.

In a blog post, Punya Misrah answered when asked about Problem-based Learning (PBL) as follows: “There is a value to small group activities as there is to a lecture. Clearly if we are emphasizing higher order thinking skills – we need to move away from simple rote-learning tasks. It is just that TPACK per se does not lean one way or the other. It does not speak to the broader goals of education. Some of my more recent work has focussed on these broader goals – and in that there are many areas (if not all) that we will be in agreement. Finally, TPACK does not privilege content, technology or pedagogy. In fact we have argued (quite clearly) that there are situations where one or the other is the driver.”

Misrah states unequivocally that TPACK is not a goal-oriented model. Educators may find an issue with the argument of taking a neutral stand on pedagogy. If we seek to educate strong self-directed, lifelong learners that build in intrinsic motivation in order to thrive in a digitized and globalized society 4.0, then a merely instructional or a behavioristic approach is not an option. Given that the goal of education is the empowerment of learners of all ages, TPACK cannot have it both ways.

Mishra is also wrong assuming an arbitrary relationship between pedagogy and technology. Empirical research has demonstrated that teacher-centred instructors use technology in significantly different ways as compared to instructors willing to change form and content of curricula via digital media or colleagues who have embraced constructivist forms of pedagogy (Schaumburg, 2003). His mention of online discussions as an example of technology driving pedagogy is not correct insofar e.g., teachers advocating rote-learning would simply not bother using online chats to disrupt their practice. It is the pedagogical attitudes of teachers that determine the use of technology in the classroom, not vice versa. Technology is not neutral in this regard since technology either supports or distracts from facilitating learning processes and achieving learning outcomes. In an arbitrary framework that facilitates any system, there can be no definite criteria for the usefulness or quality of digital media.

Conclusion: TPACK’s Lack of Empirical Support and Scientific Usefulness

While some academics find in TPACK a universal model for curricula design, teachers in the field may find it cumbersome, misleading and confusing to sort out the finer details of TK, PK, CK, TPK, PCK, TCK and TPACK in order to progress with the design of learning units. In their empirical study comprising 596 online teachers across the United States, Archambault and Barnett (2010) could not verify pedagogy, content and technology as three separate domains. The only domain that distinguished itself from the others was technology.

As expected, teachers struggled with making clear inferences to the overlapping fields. The authors specified the following example: “Three online teachers were challenged with separating out specific issues of content and pedagogy. For example, Item d – “My ability to decide on the scope of concepts taught within my class” was interpreted by two teachers as belonging to the domain of pedagogical content rather than content alone. The same misinterpretation happened with Item b – “My ability to create materials that map to specific district/ state standards.” The participants saw this item as a part of pedagogy content (Archambault & Crippen, 2009). These examples, coupled with the results from the factor analysis, support the notion that TPACK creates additional boundaries along and already ambiguous lines drawn between pedagogy and content knowledge.” (p. 1659)

TPACK, to state the obvious, is not a theory that can be empirically verified. There are no dependent or independent variables defined (since all components are regarded as equal with no clear causal or conditional relationships implied), there are no null-hypotheses to test and there are no evidence-based outcomes to predict. TPACK failed in initial empirical research distinguishing its seven mutually separated domains. Most worryingly, however, the model offers neither a goal-directed framework that is concerned with the empowerment of learners in the use of digital technology, nor is it concerned about the effects of digital socialization trajectories on society.

It still needs to be demonstrated that the positive effects attributed to TPACK are inherent effects of the model and not based on the critical reflections that are bound to come up when educators discuss the integration of digital media for the classroom.

 

References

Archambault, L.H. & Barnett J.H. (2010). Revisiting pedagogical content knowledge: Exploring the TPACK framework. Computers & Education, 55 (2010) 1656–1662

Brown, T., & Katz, B. (2009). Change by design: How design thinking can transform organizations and inspire innovation. New York, NY: HarperCollins Publishers.

Koehler, M., & Mishra, P. (2005). What happens when teachers design educational technology? The development of technological pedagogical content knowledge. Journal of Educational Computing Research, 32(2), 131–152.

Koehler, M., & Mishra, P. (2008). Introducing TPCK. In AACTE Committee on Innovation and Technology. (Ed.), Handbook of technological pedagogical content knowledge (TPCK). New York: Routledge.

Liedtka, J., Salzman, R., & Azer, D. (2017). Design thinking for the greater good. New York: Columbia University Press.

McEwan, H., & Bull, B. (1991). The pedagogic nature of subject matter knowledge. American Educational Research Journal, 28(2), 316–334.

Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: a framework for integrating technology in teacher knowledge. Teachers College Record, 108(6), 1017–1054.

National Council for Accreditation of Teacher Education. (2001). Professional standards for the accreditation of schools, colleges, and departments of education ([Electronic version]. Washington, DC: Author. Retrieved June 27, 2004, from http://www.ncate.org/2000/2000stds.pdf

National Science Teachers Association. (1999). NSTA standards for science teacher preparation [Electronic version]. Arlington, VA: Author. Retrieved June 28, 2004, from http://www.iuk.edu/faculty/sgilbert/nsta98.htm

Plattner, H., Meinel, C., & Leifer, L. J. (2016). Design thinking research: Making design thinking foundational. Cham, Switzerland: Springer

Rowe, P. G. (1998). Design thinking. Cambridge, Mass: MIT Press.

Schaumburg, H. (2003). Konstruktivistischer Unterricht mit Laptops? Dissertation. Berlin: Freie Universität Berlin. Internet-Dokument: http://edocs.fu-berlin.de/diss/receive/FUDISS_thesis_000000000914?lang=en [5.6.2015]

Segall, A. (2004). Revisiting pedagogical content knowledge: the pedagogy of content/the content of pedagogy. Teaching and Teacher Education, 20(5), 489–504.

Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.

Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1–22.

 

 

Why Solidarity? Notes on Social Justice

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FILES-US-STOCKS-MARKETS-OPEN

Our Natural Sense of Solidarity is Both Necessary and Compromised

Our natural sense of solidarity with others is situationally compromised, no matter how self-glorifying we perceive ourselves. While we generally feel great empathy for the suffering of others such as e.g., on TV as we see children ripped apart by bombs in war zones or scrapping for food on third-world garbage landfills, we also close our eyes with ease once we are asked to pay a price for solidarity.  Refugees should receive help by the UNHCR, we all agree, but better not knock at our doorsteps. We know very well about the difficulties of countries such as Italy or Greece to deal with the influx of refugees and the unbalanced demands of the Dublin treaty, but conveniently close our eyes at the remote prospect of having to share the burden. We opt for solidarity when it suits us and as long as it does not cause personal inconvenience. Our natural sense of solidarity, mirror neurons activated or not, appears only of limited use to solve issues of social justice. Natural perceptions of human solidarity are easily corruptible, especially by politics.

Three Principles of Social Justice

Generally, we are dealing with three principles of social justice: the principle of need, the principle of performance and the principle of participation.

The principle of need says that people should get what they need. Like in Maslow’s hierarchy of needs, people need to be provided food and shelter, comfort and respect. This is fair. From a Machiavellian perspective we could state that unless the poor are not comfortably poor, social unrest will knock on the door. Typically, we all agree that people should be provided with the basics of life, such as medical care and education. Social justice is served when people are provided what they need.

The principle of performance says that it is justice when an effort is rewarded. We commonly hear two variations of this principle. (1) On a negative attributional note, it says: Someone ambitious cannot be treated the same as someone lazy. Be it grades at school or salaries – those who put in an extra effort need to be rewarded while others even may get punished. (2) On a positive normative note, it says: People are not equal. Some are content to live simple, happy lives while others are ambitious to live more demanding lives that also involve higher responsibilities. A surgeon should be paid more than a taxi driver because, obviously, a surgeon carries greater responsibility and requires years and decades of qualification while a taxi driver does not. When I visited China in 1985, corrupt taxi drivers actually made more money than doctors at a hospital, so assumptions on the principle of performance should not be taken for granted.

The principle of participation says that justice is when all people can participate in society and nobody should be excluded. People from socially disadvantaged backgrounds should enjoy going to the movies, theatres, order a pizza, not be discriminated against etc. just like anyone else. Disadvantaged socioeconomic groups should not be worn down to the point where they stop voting, lose hope in a better future, limit their world to immediate physical needs to merely live in the here and now. Participation entails the empowerment to contribute to the whole of society and to play an active part in it.

Conflicting Principles – Now What?

It is easy to figure out that although we generally agree on the justification of these noble and sensible principles, they can conflict with one another. Let’s take capital gain for instance. If I work hard enough, I accumulate capital. This capital is earned by my blood, sweat and tears and I shall, therefore, enjoy the benefits. So goes the traditional libertarian narrative. Soberingly, stock markets do not know blood, sweat and tears. They know numbers, patterns and trends, investor sentiments and market dynamics, but not private perspectives. Once people have accumulated enough capital, such as by inheritance, they do not need to work anymore for anyone, at least not directly. The non-working rich contrasted against the working poor violate the principle of performance: It is against the principle of performance that people enjoy the benefit of a luxurious and comfortable life without effort and qualification. In dictatorial states, such as North Korea, a small corrupt elite manages a different system in which people are rather poor together (low needs satisfaction, high social inclusion cemented by ideology) while performance is measured by the loyalty to the regime.

Currently, the USA under Trump promotes a Rusian model favouring oligarchs, reminiscent of feudal societies of the Middle Ages. In this system, a caste of ultra-rich and powerful individuals and multinational corporations govern the rest of society. Democratic institutions, such as the judiciary or media, are hollowed out and remodelled to serve the reigning caste. Trump and Putin essentially share the same idea of society. Eventually, large parts of the population become socially and politically excluded (and alienated), they will not be able to satisfy basic needs and, in addition, experience how honest effort and hard work do not translate into a better life. People work hard but stay poor. The American Dream has come to its end. How does this compare to North Korea where a large part of the population prefers staying poor together, united under a God-like leader?

The cited examples share a common trait – they are based on inequality. This is not inequality in people being different, which by nature they are, but by attributed inequality (in this case the ‘entitled’ versus the ‘non-entitled’ members of society). Inequality corrupts all three principles of social justice. In a society where rich and powerful elites reign there can neither be a fair gain by effort (since wealth and power are inherited and stays within the caste) nor can there be a fair social participation (little or no social mobility, Wallmart for the poor and luxury brands for the rich) nor can there be a fair distribution of social goods such as medical services, housing, a decent infrastructure, democratic political influence and education. Perversely, it is even beneficial to reigning elites if the rest of society stays poor, uneducated and easily controllable. Recent technological advances in Big Data and AI provide dominating classes with powerful tools to predict social motivation and to influence groups’ decision-making.

Why Solidarity?

Kant would have argued that since we are agents of reason, we cannot use others as a means to an end. Applying the Categorical Imperative, it would be immoral to instrumentalise others, or groups of others, for one’s personal benefit and gain since we would violate their autonomy as well as betray our own. But why should powerful elites care about Kantian reason?

The Habermasian argument goes deeper. Reasoning, unlike a Kantian metaphysical concept, renders for Habermas as a function of intersubjectivity and underlies social action. No matter how unequal and suppressive a regime might be, it can never fully silence questions of legitimisation and justification. Which good argument (that is not ideological and arbitrary) could legitimize inequality? Stakeholders in society can raise the questions of a fair mediation of applied principles at any time. Emancipating inquiry, suppressed or endorsed, is in principle always open and possible. Only in a society that is based on solidarity, claims to social justice can be mediated amicably, peacefully and productively. The process of globalisation supports Habermas’ view since questions about the legitimisation of systems and the distribution of wealth are amplified in an interconnected world – or ‘spaces of flow’, as Manuel Castells put it.

Beyond demonstrating the logic of solidarity via a communication-bound rationality, I could think of another good argument. This is that in a diverse, open pluralistic society any public solution-development tends to be superior and more sustainable as compared to single-minded propositions by dominating groups. Social coherence, shared wealth, democratic innovation and a future-oriented design of institutions are best mitigated within conditions of solidarity where stronger members openly support and foster the disadvantaged of society. We may in the short term suppress the perspectives of others, but only at the cost of negative long-term ramifications.

The more perspectives are invited to a discourse, the more differentiated and fair policies can be designed. Let’s call it the cultural problem-solving argument. It asks what kind of society we want to live in and which benefits, or suffering, we are we willing to impose on our members. In economics, solidarity is realized by a social market economy. It aims at empowering, not alimenting, the less well-off members of society.

Conclusion: Social Trust versus Living in Fear and Paranoia

Although principles of social justice compete with one another, we cannot escape the social and cultural conditions that we create. Solidarity, as an underlying expression of unity and framework to mediate claims to social justice, is a prerequisite for unbiased public discourse.  Solidarity goes beyond the weighing of interests or the balancing of influence since it aims at a cooperative system design. Cooperative design creates, beyond more sustainable solutions, the precious outcome of social trust. Correspondent Jan-Philipp Sendker illustrated the argument by telling the story of a Chinese billionaire (Talkshow with Markus Lenz on German TV, ZDF, on 30. November 2017).

Despite his incredible wealth, this man lived in constant fear of authorities and even of his family who, according to him, could not be trusted with taking care of his autistic son after he and his wife would die. This is how he asked Jan-Philipp Sendker if he could assist him applying for German citizenship. He had heard in the media that Switzerland, New Zealand and Germany were among the few countries where handicapped children could be institutionalized and trusted but not Chinese institutions. Not all the money and power in the world was able to provide him with the comfort of public trust.

Solidarity among all groups of society is a prerequisite for social justice. This is how divisive and polarizing populists run down countries by turning them into plutocratic oligarchies. It is not a trajectory that open Western societies want to follow.

What the OECD Findings on Students’ Collaborative Problem-Solving Skills Tell Us … And What Not

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PISA-CPS-Girls-vs-BoysCongratulations, Singapore!

As one of the pioneers that have championed Problem-Based learning (PBL) in Singapore, I was delighted to see Singapore on the No.1 spot in collaborative problem-solving when the OECD (OECD, 2017) presented its results on November 21, 2017. Many years of hard work by consultants like myself and government investment into a more student-centered pedagogy have, obviously, paid off.  Still, we need to be prudent on how to interpret the results since there is much more implied in the study than meets the eye.

Lesson No.1: Collaborate problem-solving is the exception to the rule, even among top performers

Graphs, like the one above, seldom tell us the overall picture. One remarkable key-finding of the study was  that (highlights by me) ‘(…) on average across OECD countries, not even one in ten students can handle problem-solving tasks that require them to maintain awareness of group dynamics, take initiative to overcome obstacles, and resolve disagreements and conflicts.’ (OECD, page 5). The study points out that even for top-performer Singapore only one in five students attain a high level among the cited criteria, while three-quarters of students are able to address problems of medium difficulty and can integrate diverse social perspectives. Collaboration as a key competence of the knowledge society (Moshman & Geil, 1998) appears rudimentary in practically all developed nations. The results reveal that there is much room for improvement across the board.

The unexplained gender gap

One of the central graphics and headline presented to the media by the OECD organisation (above) suggests that girls categorically outperform boys in collaborative problem-solving skills, which is not the case. Similarly, in a previous study, boys were found to outperform girls in individual problem-solving. Gender differences are statistically significant, but as in all statistics, this means that in reality there is still a large overlap between the better performing boys and the not so well performing girls (or vice versa, when looking at individual problem-solving skills). The authors of the study do no try to explain the international gender gap. They speculate that girls might simply be more receptive to interpreting nonverbal cues (Hall & Matsumoto, 2004; Rosip & Hall, 2004) since the gender gap cannot be explained sufficiently even after accounting for better reading literacy among girls.

Another reason might be found in different age-related competencies between boys and girls. Girls tend to mature faster than boys. This is how longitudinal analyses would be in a better position to explain underlying developmental factors. Judging from my experience with adolescent students, the gender gap diminishes as student populations grow older. In support of this hypothesis, the earlier maturation in girls has been associated with different neurological development (Lim et al., 2015). If varying neurological development could be identified to impact collaborative skills, the gender gap might not qualify as a solid predictor of collaborative skills in adulthood as data may suggest at first sight (see Figure V.4.4 below).

Looking at top-performers Singapore, Japan and Korea, the cultural influence on collaborative skills in interdependent Asian societies (Fiske et al., 1998) who also assign a high social value to education would be another worthwhile topic of investigation. As can be concluded from data, girls do slightly better than boys while some cultures do notably better than others. However, cultural differences clearly outweigh gender differences.

by gender

The big question: Is learning still enjoyable?

To facilitate lifelong learning, learning itself should be an enjoyable, motivating and insightful process. Learning should take place within a positive social environment and it needs to develop students’ personal resources. Although the significant effect of positive social relations for collaborative skills has been emphasised in the OECD study, there is no explicit connection drawn to problem-solving made in the classroom.

The generally stricter and more rigid learning environments in Singapore classrooms do not compare, by a wide stretch, to the more explorative and intrinsic motivation-based classrooms in Finland. This is how, to me, the psychological winner of the OECD study is Finland. Finland demonstrates that a nation can be a leader in collaborative problem-solving while advocating a student-centred, active learning pedagogy at the same time. This fact leads to another scientific blind spot, which is the issue of developing a sustainable intrinsic motivation to solve professional and personal problems throughout the lifetime. In the meantime, the successful alternative approach in Finland has been recognized in Singapore on a ministerial level (Sinnakruppan, 2017).

Lesson No.2: Problem-solvers are not necessarily innovators and entrepreneurs

With the promotion of collaborative problem-solving skills, Singapore had hoped to create an innovation hub reminiscent of an SE-Asian version of Silicon Valley. Although Singapore students fare well in problem-solving, innovation and entrepreneurship did not materialize to the extent it was anticipated by the government. Some factors inhibiting innovation appear to be the cultural habit of relying on a centralized administration, the unwillingness to take risks and to exchange ideas (Wan et al., 2005).

Although I am an ardent supporter of PBL myself, I had to learn over the years that problem-solving and entrepreneurship require different skillsets. Entrepreneurs display a high degree of frustration tolerance and are willing to take above-average risks. Entrepreneurs learn from failures, evolve advanced mental abilities to simulate future scenarios and develop high motivational levels in support of personal perseverance – all qualities that collaborative group processes do not necessarily imply. Innovators need to be brave: The truth is that more innovative ideas have also a higher probability of failure.

Summary

One of the key takeaways from the latest OECD study was that collaborative problem-solving is still in its infant stages, even among the top performers. Averages do not represent the stunning underdevelopment among practically all nations. We can agree with the authors of the OECD study that collaborating students only mature within collaborative schools. Beyond the mere measure of cognitive competencies, the development of personal resources and social skills seem to pave the way to succeed in the emerging knowledge societies.

 

References

Fiske, A. P., Kitayama, S., Markus, H. R., & Nisbett, R. E. (1998). The cultural matrix of social psychology. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (pp. 915-981). New York: McGraw-Hill.

Hall J.A. & Matsumoto D. (2004), Gender differences in judgments of multiple emotions from facial expressions, Emotion, Vol. 4/2, pp. 201-206, http://dx.doi.org/10.1037/1528-3542.4.2.201.Lim

Lim S., Han C.E., Uhlhaas P.J. & Kaiser M. (2015). Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data, Cerebral Cortex, Volume 25, Issue 6, 1 June 2015, Pages 1477–1489, https://doi.org/10.1093/cercor/bht333

Moshman D. & Geil M. (1998), Collaborative reasoning: Evidence for collective rationality, Thinking and Reasoning, Vol. 4/3, 10. pp. 231-248, http://dx.doi.org/10.1080/135467898394148

OECD (2017), PISA 2015 Results (Volume V): Collaborative Problem Solving, OECD Publishing, Paris.
http://dx.doi.org/10.1787/9789264285521-en

Rosip J.C. & Hall J.A. (2004). Knowledge of nonverbal cues, gender, and nonverbal decoding accuracy, Journal of Nonverbal Behaviour, Vol. 28/4, pp. 267-286, https://doi.org/10.1007/s10919-004-4159-6.

Sinnakaruppan S. (Nov 26, 2017). Why Singapore’s education system needs an overhaul. In: Todayonline. Retrieved from: http://www.todayonline.com/daily-focus/education/why-spores-education-system-needs-overhaul

Wan D., Ong C.H. & Lee F. (2005). Determinants of firm innovation in Singapore, In: Technovation, Volume 25, Issue 3, 2005, Pages 261-268, ISSN 0166-4972. Retrieved from: https://doi.org/10.1016/S0166-4972(03)00096-8.

About the Methodology of Social Change

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cooperation2

The emergence of cooperative research

The Problem of Traditional Research Cycles

The biggest challenge to social sciences is the alienation between research and application. In social sciences and education, few empirical findings find their way back into improving everyday life and not everything that is being researched is of relevance in real-world settings. The gap between theory and application, between expert cultures and social actors, is grounded in traditional belief models.

Typically, it is assumed that a social problem, which is identified by researchers and decision makers, requires quantitative analysis to be objectively addressed. The tool of choice is empirical studies that reveal the causal, correlational and conditional relations of a social problem, e.g., by employing statistical methods such as regression models or structural equation models. At the bottom of the scientific hierarchy, we find smaller single-case studies, followed by correlational research, quasi-experimental studies and on the top meta-analyses and Randomized Controlled Field Trials (RCT/ RCFT). The latter is highly structured for the quality of reporting ‘enabling readers to understand a trial’s design, conduct, analysis and interpretation, and to assess the validity of its results.’ (CONSORT, 2010).

A problem that science despite all rigour, however, cannot reflect is the roles of involved decision-makers who interpret and apply scientific studies on behalf of their employees, staff as well as their users. A good example was the PISA studies that had a major political influence on educational governance across Europe. Local school principals, teachers, parents and students could only watch in disbelief how politicians surrendered to a merely economized view of education based on the measurement of few scant competencies. The top-down approach of monopolized research implies a number of problems when looking at developing sustainable social solutions. The most prominent critique is that social actors are degraded to mere data sources, for example via survey-based research, but they are not included as actively contributing rational agents.

This imposes four fundamental limitations to improving social systems:

(1) CONSTRUCT COMPLEXITY AND ENVIRONMENTAL VALIDITY: The complex nature of people’s lifeworld (their needs, interests, perspectives, motivations, ideas and wishes) is excluded in traditional research. The traditional objectifying approach, e.g., by limiting questions and potential answers of social actors to Likert Scales, imposes a reductionist perspective right at the beginning of the research process.

Traditional research implies a number of conceptual assumptions that may not coincide with the context in situ. Quantitative studies work well in settings where a problem can be clearly defined beforehand, such as medical problems with experimental- and control groups, but they necessarily fail in the case of social solutions development that depends on the active and adaptive contributions of social actors. In this case, the relation between research and research subjects is of an entirely different nature – one is descriptive while the other is participatory.

(2) DISEMPOWERMENT: This argument states that social actors are denied ownership of the problem at hand. By circumventing the perspectives of affected parties, participants of social transformation are excluded from the discourse to improve their environment. As a result, frustration and resentment may set in as participants are degraded to passive onlookers on the implementation of top-down policies. In addition, research and policy-making lose individual and collective meaning which otherwise would have emerged via active participation.

(3) SOCIAL INPUT AND DEVELOPMENT: Traditional empirical research rests on a number of unreflected assumptions. For example, which should be the desired effects of an intervention from the perspective of social actors, which noticeable criteria can measure these effects and which local resources are available to accommodate improvements? It is one perspective to abide by professional standards, such as promoting the acquisition of competencies or to develop individual autonomy, and another to empower social actors to work on solutions taking their unique cultural tools, personal motivation and local resources into account.

(4) SUSTAINABLE OUTCOMES: Social models that are created in cooperation naturally develop areas of corresponding responsibilities. Systems, where policies are imposed onto populations externally, suffer from the drawback that nobody feels in charge of managing them due to the lack of ownership. People assume responsibility for the systems they create.

The development of participatory models

We may take schools as an example. How a school deals with diversity, heterogeneity, its psychological climate, social support networks or mechanisms for continuous improvement is entirely based on the coordinated effort of the administration, teachers, students, their parents and the social context at large.

Due to the need for consensus, social problems and their consequences need to be set in relation qualitatively (Which is the meaning of problems to the individual and which are their social impacts ?) as well as quantitatively (How do problems scale in the social sphere?). Typically, such questions are more adequately addressed in mixed study designs. Mixed-study designs, unlike large quantitative studies, recognize the complexity of social transformations but still fall short of the criteria of democratic empowerment and cooperative local development. New innovative scientific approaches that are based on the input from social actors will be discussed in Part 2 of this essay.

From a data perspective

From a data perspective, we could state that in traditional research a fixed statistical method, based on collected data sets, is employed to determine the significance of effects or the causal interdependencies of factors within a construct. By contrast, in cooperative research, we need to design logical operators (methods) in such a way that we derive at a measurable, computable result. The traditional statistical method is fixed by its choice of initial study design, while cooperative efforts are based on the premise of performing adaptive process changes (competitive prototyping) in regards to achieving desired outcomes.