As with all areas of society, education has been greatly impacted by the Covid-19 pandemic. Those of us who work in Higher Education (HE) have needed to rapidly reconfigure existing on-campus modules in order to render them suitable for online delivery. The fact that virtual learning environments (VLE) have been embedded across UK Higher Education Institutions (HEIs) to support course management and on-campus delivery for some time (Walker, Jenkins & Voce, 2018) has meant that many HEIs could adapt relatively quickly. We would argue, however, that successful and sustainable blended learning is about far more than simple digital competencies. The move online has triggered a set of secondary issues, with many students expressing ambivalence towards these enforced changes, and some disciplines questioning how they can appropriately mould pedagogy to meet their specific needs.  

Previous research (Petronzi & Hadi, 2016) analysing feedback from online learners has identified academic and peer interaction as notably enhancing learner experience, subsequently improving retention and completion levels (Hadi & Rawson, 2016; Hone & El Said, 2016). Classroom learning environments that are perceived to be highly personalised and encouraging of active participation not only stimulate deep level learning (Matsushita, 2017), they increase both a sense of belonging and motivation. Indeed, Witney and Smallbone (2011) question the value of digital learning if the technology does not promote collaboration. Blended approaches must therefore deliver a balance between technology-mediated and human-mediated options in order to ensure that student’s social learning needs are met (Anthony et al., 2020).

It is also important to acknowledge that lecturers imparting information does not necessarily equate with students acquiring knowledge. ‘Traditional’ lecture formats, where students are engaged in passive activities (such as reading or listening to information with no personal salience), lead to low cognitive arousal, followed by shallow learning or disengagement. Positive affective states on the other hand, improve attention leading to detailed encoding (Dunne & Opitz, 2020). This elaborative encoding subsequently aids retention and recall (Baddeley et al., 2016). It is therefore important that pedagogy encompasses both knowledge transmission and collaborative opportunities that will consolidate, challenge and expand understanding through applied and socially constructed processes (Aubrey & Riley, 2019).

From both a constructivist (Bates, 2016) and cognitive perspective (Bein, Trzewik & Maril, 2019), learning would appear to be largely incremental, with learners making sense of new information by building on prior knowledge. Memory can be split into three major components:[1] encoding, [2] storage, and [3] retrieval. In order to create usable knowledge, the learner must successfully harness both their attentional resources and concentration in order to encode new information. By their very nature, these resources are fragile and transitory, rendering them responsive to brief, focussed and varied stimuli such as can be provided by thoughtful use of individual asynchronous activities (Petronzi & Petronzi, 2020).

When the learner revisits the concept, this activates stratified links, consolidating existing connections and augmenting retrieval. Students are more likely to experience deep level processing if the task involves semantic coding, more consequential analysis and greater elaborative rehearsal (Craik & Tulving, 1975; Unsworth, 2015). Specificity of processing (Vaidya, Zhao, Desmond & Gabrieli, 2002) and self-reference (Symons & Thompson, 1997) also appear to aid recall, as does the use of chunking (Campoy & Baddeley, 2008) and elaborative questioning (Roediger & Pyc, 2012). However, the purpose and unique contribution of each blended element may need to be identified and explained to avoid asynchronous elements being perceived as ‘additional’ to the tutor led learning (Ustun & Tracey, 2020).

Despite design challenges, we would also argue that blended learning can minimize extended periods of passivity such as can be observed in traditional classrooms (Baepler, Walker & Driessen, 2014). Such active learning enhances student engagement, self-management and learning (Means, Toyama, Murphy, Bakia, & Jones, 2010) all of which ultimately improve student satisfaction (Hyun, Ediger & Lee, 2017).  

The OaC Model (Petronzi & Petronzi, 2020) thus proposes:

It is recognised that the OaC model requires discipline-specific adaptations and application. The content and style of delivery should constructively align with the programme, module and assessment outcomes (Biggs & Tang, 2011), each of which will be impacted by the discipline intentions. For instance, problem-solving, active and collaborative learning in campus-based sessions (Ustun & Tracy, 2020) would diverge for teacher education in comparison to nursing. The former would focus on application of knowledge to progress children’s learning, and the latter on professional capacities for patient wellbeing and care. Furthermore, the campus experience may require specific resources or access to specialist facilities. Engineering, for example, must ensure that industry standards are adhered to and this can only be done if students follow a process similar to the OaC model, wherein they experience a mixture of practical and lab-based learning together with theoretical sessions (Ożadowicz, 2020)

Further considerations must include the psychosocial learning environment (Vayre & Vonthron, 2017). The Mind.org.uk (2020) survey identified how loneliness can negatively impact mental health, with 73% of students reporting a decline in their mental wellbeing during lockdown, Baloran (2020) thus proposed that HEIs should strengthen their responses to pandemics and consider innovative approaches to support socialisation and reduce mental health issues. There is also a need to remain mindful that some students may be facing competing demands on their time, and that those from more disadvantaged backgrounds may also lack the environment and technical resources required to fully flourish.

We therefore, conclude that blended learning comprising a range of synchronous and asynchronous activities can augment student learning and reduce the sense of isolation experienced by so many over the past year. However, we acknowledge that preparation places many demands on staff time and HEIs must support tutors with making pedagogical decisions and disseminating best practice. Theoretical models, such as the OaC model (Petronzi & Petronzi, 2020) could support with planning and delivering blended approaches, but discipline specialists will need to consider how best to support their learners through this approach. Moreover, prior to, or in conjunction with the implementation of a widely adopted blended approach, HEIs will likely also need to consider the previous learning experiences of the student population and their readiness for autonomous learning. Indeed, it may be that a transition period or gradual integration of a blended model is required in the first instance, as we begin a potential redefinement of studying at university.  

Authors

Rebecca Petronzi, English Subject Lead and Lecturer in Primary ITE Institute of Education; Dr Dominic Petronzi, Lecturer in Psychology; and Dr Kay Owen, Lecturer, Institute of Education, University of Derby.

Illustrations

Beth Robertson

References

Anthony, B., Kamaludin, A., Romli, A., Raffei, A. F. M., Phon, D. N. A. E., Abdullah, A., & Ming, G. L. (2020). Blended learning adoption and implementation in higher education: A theoretical and systematic review. Technology, Knowledge and Learning, 1-48. doi: 10.1007/s10758-020-09477-z.

Aubrey, K., & Riley, A. (2018). Understanding and using educational theories. (2nd Ed). London: Sage.

Baddeley, A., Eysenck, M.W., & Anderson, M.C. (2016). Memory (Second Edition). London, Psychology Press.

Baepler, P., Walker, J. D., & Driessen, M. (2014). It's not about seat time: Blending, flipping, and efficiency in active learning classrooms. Computers & Education, 78, 227-236. doi: 10.1016/j.compedu.2014.06.006.

Baloran. E. T. (2020). Knowledge, Attitudes, Anxiety, and Coping Strategies of Students during COVID-19 Pandemic. Journal of Loss and Trauma, 25, 635-642. doi: 10.1080/15325024.2020.1769300.

Bates, B. (2016). Learning theories simplified –and how to apply them to teaching. London: Sage

Bein, O., Trzewik, M., & Maril, A. (2019) The role of prior knowledge in incremental associative learning: An empirical and computational approach. Journal of Memory and Language 107, 1-24

Biggs, J., & Tang, C. (2011). Teaching for Quality Learning at University. (4th Ed). Maidenhead: OUP.

Campoy, G., & Baddeley, D.E. (2008). Phonological and semantic strategies in immediate serial recall. Memory, 16 (4), 329-340.

Craik, F.I.M. & Tulvig, E. (1975). Depth of processing and the retention of words in episodic memory. Journal of Experimental Psychology: General, 104 (3), 268-294.

Dunne, L., & Opitz, B. (2020). Attention control processes that prioritise task execution may come at the expense of incidental memory encoding. Brain & Cognition, 144, 105602. doi: 10.1016/j.bandc.2020.105602.

Hadi, S. M., & Rawson, R. (2016). Driving learner engagement and completion within MOOCs: a case for structured learning support. Paper presented at the European MOOCs Stakeholder Summit.

Hone, K. S., & El Said, G, R. (2016). Exploring the Factors Affecting MOOC Retention: A Survey Study. Computers and Education, 98, 157-168.

Hyun, J., Ediger, R., & Lee, D. (2017). Students’ satisfaction on their learning process in active learning and traditional classrooms. International Journal of Teaching and Learning in Higher Education, 29, 108-118

Matsushita K. (2018) An Invitation to Deep Active Learning. In: Matsushita K. (eds) Deep Active Learning. Springer, Singapore. https://doi.org/10.1007/978-981-10-5660-4_2

Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115, 1-47.

Mind (2020). The mental health emergency: how has the coronavirus pandemic impacted our mental health? London: Mind. Available at: mind.org.uk Ożadowicz. A. (2020). Modified Blended Learning in Engineering Higher Education during the Covid-19 Lockdown – Building Automation Courses Case Study in Education Sciences, 10, 292. doi: 10.3390/educsci10100292

Petronzi, D., & Hadi, M. (2016). Exploring the factors associated with MOOC engagement, retention and the wider benefits for learners. European Journal of Open, Distance and E-Learning, 19. doi: 10.1515/eurodl2016-0011.

Petronzi, R., & Petronzi, D. (2020) The Online and Campus (OaC) model as a sustainable blended approach to teaching and learning in higher education: A response to Covid-19. Journal of Pedagogical Research. doi: 10.33902/JPR.2020064475.

Roediger, H.L., & Pyc, M.A. (2012). Inexpensive techniques to improve education: Applying cognitive psychology to enhance educational practice. Journal of Applied Research in Memory and Cognition, 1, 242-248.

Symons, C.S., & Thompson, B.T. (1997). The self-reference effect in memory: A meta-analysis. Psychological Bulletin 121 (3) 371-394. doi: 10:1037/0033-2909.121.3.371

Unsworth, N. (2015). Working memory capacity and recall from long-term memory: Examining the influences of encoding strategies, study time allocation, search efficiency and monitoring abilities. Journal of Experimental Psychology: Learning, Memory and Cognition. doi.org/10.1037/xlm0000148

Ustun, A. B., Tracey, M. W. (2020). An effective way of designing blended learning: A three phase design-based research approach. Education and Information Technologies, 1529–1552. doi: 1007/s10639-019-09999-9.

Vaidya, C.J., Zhao, M., Desmond, J.E., Gabrieli, J.D.E. (2002). Evidence for cortical encoding specificity in episodic memory: Memory-induced re-activation of picture processing area. Neuropsychologia, 40 (12), 2136-2143. doi 10.1016/S0028-3932(02)00053-2.

Vayre, E., & Vonthron, A-M. (2017). Psychological engagement of students in distance and online learning: Effects of self-efficacy and psychosocial processes. Journal of Educational Computing Research 55, 197-218.

Walker, R., Jenkins, M., & Voce, J. (2018). The rhetoric and reality of technology-enhanced learning developments in UK higher education: reflections on recent UCISA research findings (2012–2016). Interactive Learning Environments, 26, 858-868. doi: 10.1080/10494820.2017.1419497.

Witney, D., & Smallbone, T. (2011). Wiki work: Can using wikis enhance student collaboration for group assignment tasks? Innovations in Education and Teaching International, 48, 101-110. doi: 10.12691/education4-6-4.