Tag: ECT

Deep Learning vs Surface Learning

Posted on by richardjamesrogers

An article by Richard James Rogers (Award-Winning Author of The Quick Guide to Classroom Management and The Power of Praise: Empowering Students Through Positive Feedback)

Illustrated by Sutthiya Lertyongphati

Accompanying podcast episode:

I’m currently working through an excellent online course offered by the University of Queensland via EdX. The course is entitled ‘Deep Learning through Transformative Pedagogy‘. It’s absolutely fascinating and I would highly recommend the course for any teacher who is serious about helping students prepare for examinations, catch-up on missed work or understand complex content.

In today’s blog post I aim to share:

  • What I have learned about deep and surface learning from the course so far
  • Some practical ways in which deep learning can be encouraged in the classroom

So, get ready for a deep dive into this compelling topic!

A brief history behind the development of deep learning practices (and why surface learning is no longer enough)

The course began with brief history of schooling, and how technology has been a key driver for the need to educate children. The point was made that surface learning (e.g. memorization of facts) may have been sufficient in the past. However, for our learners today, facts can change very quickly. Skills need to be upgraded regularly and throughout one’s life. As a result, teaching has seen a massive shift from teacher-centred approaches to those which are learner-centred. Contemporary pedagogical approaches, such as constructivism (where students are active participants in their own learning and construct new knowledge based on links to current understandings and prior fundamentals) have an important role to play in this new, digital age.

It’s important to remember throughout today’s blog post that effective and active learning are two sides of the same coin: to be effective, learning must be active. Research shows that learner-centred approaches to teaching that change and develop student thinking get better results in terms of student learning outcomes than traditional information transmission methods.

What is deep learning, and how is it different to surface learning?

Deep learning means asking big questions. When students have the opportunity to explore a topic: asking the why, what, where, when and how behind some concept, idea or process, they learn a plethora of different things and extend their knowledge and understanding.

Surface learning involves rote memorization, and I saw a lot of this happening when I worked in China. Examples included colleagues who had very high-level credentials from top universities in Asia, but who were unwilling to perform classroom practical tasks/experiments with students because either ‘the students didn’t need to do that to pass their exams’, or the teachers themselves felt nervous due to inexperience. This seemed to really show itself in one subject in particular, however: mathematics. Students would be trained to learn lots of formulae, and would be given an astronomical number of drill questions to do for homework. However, when it came to applying the mathematics to an unusual or real-life problem, many students struggled.

Since taking the online course with the University of Queensland, I’ve learnt a number of interesting facts about deep learning:

  • Deep learning often involves revisiting and reviewing a topic, and can be achieved through tasks in which students are involved in active problem-solving.
  • Neuroscience teaches us that the brain is plastic, and that chemical changes actually occur during deep learning. Deep learning involves consolidation of knowledge, and is driven by protein synthesis in the brain. Animal studies have shown that when protein synthesis in the brain is blocked, only surface learning occurs.
  • Deep learning is a process of integrating new facts we learn about the world into our existing semantic framework.
  • Deep learning can be achieved when students are given the opportunity to discover content, knowledge and skills for themselves.
  • Deep learning Involves an analysis of the information being collected, allowing a more complete understanding than surface learning can provide.

In contrast to deep learning, surface learning concerns itself only with the knowledge, ideas and content present in a curriculum. Deep learning is all about relating or extending all of that. This surprised me to some extent, as I thought that learning high-demand content (e.g. redox equations in IB Chemistry) would be considered deep learning, when actually it’s just surface learning (even though the content may be considered ‘advanced’). Deep learning would occur when the student is able to apply their knowledge of, say, redox equations, to unfamiliar or extended contexts  – such as when the student is tackling sub-sections of an IB HL exam paper in Chemistry, or designing and implementing an experimental investigation into the topic. 

It’s important to note that there isn’t a clear-cut distinction between surface and deep learning: rather, there exists a gradation between one and the other. A progression is made from having an idea to having many ideas (surface learning), to relating and extending those ideas (deep learning).

Whilst the progression from surface learning to deep learning follows a continuum, it is also cyclical – as students begin to relate and extend ideas, they come up with new ideas which brings them back to the surface learning part of the cycle.

What kinds of activities can teachers do in the classroom to encourage deep learning to take place?

  • The Flipped Classroom: This was something completely new to me which I discovered on this course, and it was really enjoyable to learn about this novel approach to teaching and learning. The basic idea is that pre-reading is done at home and homework is completed in class! The students come to class already prepared with some fundamental knowledge, and then complete activities based upon what they have read. Collaborative activities (e.g. using Padlet) are really good for getting students to reflect on their learning. In terms of the pre-reading to be done at home – this doesn’t actually have to be reading. Short, 5 minute videos that the students have to watch may be enough.
  • Give students some prompt material (e.g. a website to use, an information sheet, etc.) and ask students to CREATE something from it. Good things to create include a Google Slides presentation, a Google Site, a Google Doc summary, an infographic, a stop-motion animation, a quiz (e.g. a Kahoot!) and so on. Please note: If you ask students to create something, then make sure they present it to the class in some way (e.g. a short talk). Students can work in groups for activities like this. I’ve written a separate blog post about encouraging creativity in the classroom here.
  • Since deep learning can be achieved through revisiting and reviewing content and skills regularly, journaling and past-paper practice can meet the necessary requirements. With past-paper practice, however, make sure that the students make full corrections, and can somehow articulate why they made made mistakes. The process of completing, correcting and reflecting on past-exam paper questions (or exam-style questions) is a problem-solving sequence in and of itself – hence a deep learning activity.
  • Practical work that allows students to explore an unusual context, or an extended part of a topic, can definitely encourage deep learning to take place – especially if the students have been involved in the creative design of the task themselves in some way. Think about opportunities you can create for students to design and implement their own experiments, presentations, model-building and practical/hands-on work (e.g. welding together an iron gate, making an item of clothing, building the circuitry for a small radio – it will depend on the subject you teach, of course).

Recommended further reading

Constructivism: Creating experiences that facilitate the construction of knowledge. The University of Buffalo. Accessed: 23rd May 2022.

We welcome you to join the Richard James Rogers online community! Join us on Facebook and Twitter for regular updates, giveaways of Richard’s books, special offers, upcoming events and news. 

Cognitive Challenges of Language Learners in the Digital Age

Posted on by richardjamesrogers

We must keep in-mind the unique challenges that new technologies create for all of our learners: especially those who are learning a new language, or who are attempting to access a mainstream curriculum via a second or additional language. Today, I’ve invited Tatyana Cheprasova (Senior Lecturer and EFL/TEFL instructor at Voronezh State University, Russia) to give her expert analysis of the situation, along with many excellent suggestions that we can all take on-board going forward.

This blog post has been beautifully illustrated by Pop Sutthiya Lertyongphati 

With digital technologies rapidly taking over various spheres of our lives, a new pedagogical environment for acquisition, processing and transferability of knowledge and skills has been created. These digital shifts will inevitably affect the educational sector as one of the aims of any educational paradigm is to prepare learners to face the challenges of the real world which now cannot be conceived without digital imprints and influence. This article aims to explore the cognitive challenges this new educational reality places before language learners in the Digital Age. It also attempts to provide EFL teachers with insights into how their teaching procedures can be altered in order to meet the cognitive needs of ‘digital native’ learners.

In order to develop the right understanding of the factors affecting cognitive processes (such as perception, learning communications, associations, and reasoning) and the behavioural consequences for digitally native learners, it is deemed essential to explore the new educational environment within which they operate and develop.

The new pedagogical reality which integrates digital language learning (DLL), as with any educational paradigm or teaching tool, can have its own advantages and deficiencies which become visible and apparent when the context which coined a new pedagogical phenomenon is carefully scrutinised. The pedagogical settings we now operate within and which incorporate DLL need to be viewed as the natural evolutionary result of the educational development we have witnessed in the last few decades. According to Warschauer (2004:10), at the early stage, within the language learning domain of the final decades of the 20th century, computer-assisted language learning (CALL) or Structural CALL was strongly influenced by the behaviourist paradigm which shaped this type of DLL as merely stimulus-response, drill-based programmes which enhanced the learning of new vocabulary items or grammar under rigid teacher supervision. The ensuing transfer from a behaviourist to a communicative approach to language learning where meaningful interactions were given the priority also affected the whole nature of CALL design, giving rise to Communicative CALL which implied the use of computers to engage language learners in communicative activities (Warschauer, 2004:11). Finally, with the onset of integrative ICT, the technologies within the new educational paradigm have moved into the era of Integrative CALL which relies on agency and interactive communications (both of teachers and students) as an effective pedagogical tool to solve real-life tasks and problems in a community of peers on the internet (Warschauer, 2004:11).

The widespread implemetation of Integrative CALL which has soared in the field of ELT in the last two decades has been seen by many researchers as a mainly positive trend which has a lot to offer ELT practitioners in various educational contexts (Li and Lan, 2021). Thus, as argued by Grosjean (2019) and Al-Ahdal, (2020), the incorporation of AI and Big Data used in various language applications can facilitate ELT in that it provides learners with real-life language use settings as well as helping to trace down their language progress via the analysis of learners’ errors in L2 writing procedures. Additionally, the use of AI can lead to a more individualised, rather than one-size-fits-all, approach to language teaching where the pedagogical strategies and procedures are designed to meet learners’ requirements and profiles at its best (Li and Lan, 2021). Finally, mobile-assisted language learning (MALL) and game-based language-learning (GBLL) have been regarded by many scholars as possessing high teaching potential in terms of EFL outcomes as they provide students with language learning opportunities at their fingertips, anytime and anywhere, stretching beyond learning a language as limited to only traditional classroom settings (Shadiev, Zhang, Wu & Huang, 2020; Li and Lan, 2021).

Notwithstanding all of the above-mentioned advantageous implications DLL can offer as the new pedagogical dimension, both ELT practitioners and researchers have started to question its overall positive effects on learners’ cognition,  psychological and speech development, and their flux of consciousness, thus approaching the issue from both cognitive and social perspectives (Warschauer, 2004; Komlósi, 2016, Voulchanova et al., 2017; Chernigovskaya et al., 2020). The impacts these DLL-driven pedagogical settings can have on language learners are going to be discussed below.

At this point it is worth mentioning that the whole nature of the concept of ‘knowledge’ seems to have radically changed as the Cognitivism learning model has given way to the Constructivism Paradigm. Apparently, when learning occurs within a particular teaching model, the nature of knowledge evolves on the basis of how new data is generated and pedagogical assumptions about which strategies comprise the educational process, as the following comparison illustrates (see the table below):

As it is illustrated above, knowledge is no longer approached as a monolithic unit transmitted from a teacher to their students but rather as a dynamic heterogeneous construct characterised by boundless hypertextual structure where the reader (or a knowledge receiver) acts out as the author (or knowledge co-constructor) (Warschauer, 2004; Chernigovskaya, 2020).

This innovative type of knowledge might inevitably affect learners’ main cognitive processes. Indeed, as argued by the famous Russian neurolinguist Tatyana Chernigovskaya, the hypertextual nature of knowledge leads to the formation of an innovative learning environment, which she refers to as “shared consciousness”, where learners have to rely not on their memory capacity to recall various information quanta but rather on their ability to remember the source of the particular data storage, which, in turn, can seriously weaken working memory, especially that of young learners. Additionally, the hypertextual characteristics of the new type of knowledge  are believed to affect the development of learners’ reading skills as this process now implies the inclusion of critical literacy at the very early stages of their cognitive development. This represents a challenging task for young learners whose abilities to compare, contrast and analyse, as well as to make inferences, are not so well-formed as those of adult learners (Warschauer, 2004; Chernigovskaya et al., 2020). These factors might lead to the formation of new and superficially scrutinised skills of digital knowledge management which will need to be specially addressed when teaching L2 reading comprehension.

More importantly, according to Zou and Xie’s (2018) research on the integration of MALL in language learning, this new format of learning, although enhancing personal learning processes, can seriously impede learners’ attention: shortening their attention spans for learning, and therefore, affecting learners’ ability to concentrate and control their attention. In the same vein, as argued by Hsu et al. (2019), adolescent excessive use of mobile devices might have adverse effects on their abilities to integrate scientific knowledge and to make inferences, thus leaving them with a rather distorted, disintegrated and mosaic-like scientific worldview.

Furthermore, as stated by Komlosi (2016:167), the onset of DLL will urge researchers to reconsider and revisit the essence of communication as the new digital teaching paradigm has introduced radical changes in social cognition and communication in the new form of digital culture, which implies that its members operate in connected networks constituted by several types of ‘cognitive identities’. This newly coined term refers both to human and non-human social actors that function smartly and are expected to operate within a highly interlocked framework of multifaceted information flow and exchange. The agents of info-communications in the digital world are related to each other not by commonly shared cultural narratives, as negotiated within the traditional cognitive cultural anthropology, but by fragmented narratives revealed through spontaneous and rather unstable shared interests in networking, information construction and exchange, thus facilitating non-linear, multidimensional communicative interaction which can seriously impede the traditional vertical, authoritative and declarative patterns of cultural knowledge transmission (Komlosi, 2016:167). This change in the social cognition and behaviour paradigm might have adverse effects on learners’ cognitive skills as the long evolutionary process of linear information processing typical for any culturally coherent human community is now challenged by parallel and connected network-based information processing: making use of fragmented, encapsulated information chunks provided by a plethora of information sources, which, in turn, forces learners and educators to seek new strategies of information management and info-communications in novel contexts (Komlosi, 2016:168).

Conclusion

At this point, an important conclusion which can be drawn is that the wide incorporation of DLL we are witnessing now needs to be approached as an irreversible process offering a new perspective on information processing and knowledge management of language learners in various contexts.  Notwithstanding its obvious advantageous effects, DLL has already signposted certain cognitive, behavioural and communicative challenges for learners. More research providing evidence of direct comparison between learning from others and learning from digital tools is required to develop a better understanding of the standard modes and channels of language transmission in in the digital age and to conceive the cognitive and behavioral consequences of learning in digital ecosystems.

References

  • Al-Ahdal, A. (2020). Using computer software as a tool of error analysis: Giving EFL teachers and learners a much-needed impetus. International Journal of Innovation, Creativity and Change , 12(2), 418–437.
  • Chernigovskaya, Tatiana & Allakhverdov, Viktor & Korotkov, Alexander & Gershkovich, Valeria & Kireev, Maxim & Prokopenya, Veronika. (2020). Human brain and ambiguity of cognitive information: A convergent approach. Vestnik of Saint Petersburg University. Philosophy and Conflict Studies. 36. 675-686. 10.21638/spbu17.2020.406.
  • Grosjean, F. (2019). A journey in languages and cultures: The life of a bicultural bilingual. Oxford, UK: Oxford University Press.
  • Hsu, C.T., Clariana, R., Schloss, B., & Li, P. (2019). Neurocognitive signatures of naturalistic reading of scientific texts: a fixation-related fMRI study. Scientific Reports,9(1), 1–16.
  • Komlósi, L. (2016). 13. Digital Literacy and the Challenges in Digital Technologies for Learning. In D. Dejica, G. Hansen, P. Sandrini & I. Para (Ed.), Language in the Digital Era. Challenges and Perspectives (pp. 162-171). Warsaw, Poland: De Gruyter Open Poland. https://doi.org/10.1515/9783110472059-015
  • Li, P., & Lan, Y. (2021). Digital Language Learning (DLL): Insights from Behavior, Cognition, and the Brain. Bilingualism: Language and Cognition, 1-18. doi:10.1017/S1366728921000353
  • Shadiev, R., & Yang, M. (2020). Review of studies on technology-enhanced language learning and teaching. Sustainability, 12(2), 524.
  • Sidorova, I. (2019). Learning Via Visualization at the Present Stage of Teaching a Foreign Language. Astra Salvensis, 6 (1), 601-607.
  • Vulchanova, M., Baggio, G., Cangelosi, A., & Smith, L. (2017). Editorial: Language development in the digital age. Frontiers in Human Neuroscience, 11, Article 447. https://doi.org/10.3389/fnhum.2017.00447
  • Warschauer, M. (2004). Technological change and the future of CALL. In Fotos, S & Brown, C (eds.), New perspectives on CALL for second language classrooms. Mahwah, NJ: Lawrence Erlbaum, pp. 15–25.
  • Zou, D., & Xie, H. (2018). Personalized word-learning based on technique feature analysis and learning analytics. Educational Technology & Society ,21 (2), 233–244.

We welcome you to join the Richard Rogers online community. Like our Facebook page and follow us on Twitter for daily updates.