A Futures Perspective on Learning and Teaching in Higher Education: An Essay on Best and Next Practices

Abstract

Higher education is a sector that can be slow to change, yet there are significant pressures on universities and other providers to change. Learning and teaching are central to what higher education does, and pressures, such as the switch to remote learning during the pandemic and the increasing use of generative AI, are causing a reconsideration about good learning and teaching. This essay provides a futures framework to explore best and next practices in learning and teaching in higher education. Four important and influential papers and reviews are used to consider past and current views of good teaching and learning in higher education. From these, six evidence-informed teaching practices are described as examples of current best-practice views, and then these are developed into possible, plausible, probable, and preferred next practices. This essay provides a stimulus for practitioners and researchers to adopt a futures mindset for thinking about the development of teaching and learning in higher education.

1. Introduction

Education broadly is an aspect of society that changes relatively slowly; education has been described as a ‘legacy sector, where it takes years—often generations—to bring about large-scale changes of methods, practices and operations’ [1] (p. 6). For example, schools are often criticised for continuing to adopt views from a century ago [2,3] and despite predictions at the turn of the millennium (e.g., [4]) of revolution, 25 years later, schooling remains very familiar. So too, universities, with their graduation ceremonies dating back centuries, a familiar collection of faculties and resistance to substantial change in teaching and learning practice [5,6]. Yet, the COVID-19 pandemic worldwide caused at least temporary change across all areas of education and at the tertiary level in particular [6,7,8,9,10,11]. With the pandemic now finished, there seems to be not much evidence of the bold predictions at the start of the pandemic for revolution in universities [6,12]. For example, dual mode and online teaching were not only possible during the pandemic, but in many cases synchronous and asynchronous online teaching were the only way teaching and learning was conducted [8,10]. Yet, with significant physical infrastructure (think of the Ivy League, sandstone and redbrick, as ways some universities are referred to) and a sense of tradition and importance, universities have reverted to a vision of the university that is very familiar [13].

Despite the lack of impact of a major disruption like the pandemic, there are many that still see an unfilled opportunity. For example, Arndt et al. [13] (pp. 207–209) speculate about the future of universities by using a speculative diffraction thinking process (turning over an issue again and again to gain insights). They suggest that by 2041 there will be:

• Greater importance placed on the scholarship of teaching and learning.
• New power values that mean we will need university ‘leadership that could become ‘more fluid, more collaborative, able to reconfigure as needed’ (p. 220).
• ‘…challenges and opportunities of reconceptualising assessment’ that could ‘place students as co-constructors and co-designers of an educational experience that meets their diverse needs’ (p. 220). As the ‘teaching profession changes…the relationships between students and lectures needs fundamental change’ (p. 220).
• Need to change ‘that reconciles and embeds Indigenous ways of knowing alongside a shift in power relationships between ancient and newer cultures’ (p. 221).
• ‘…technological and societal changes that ripple through higher education’ and which ‘will refocus on accessibility for local and remote students globally’ (p. 221).
• ‘…government responses and funding change’ that may mean ‘more limited choices available to… [and] limits on the richness of the socio-cultural experience of students’ in universities’ (p. 221).

Higher education may be at a pivotal moment in time where disruption and innovation on many fronts are converging and creating a dynamic landscape of possibilities. Along with ongoing concerns related to increasing accountability, funding pressures, and student mobilisation and diversification [14,15,16,17], higher education is responding to unprecedented pressure and possibility with rapidly evolving and new technologies, such as generative artificial intelligence, virtual reality, and augmented reality [18,19]. Coupled with these, students now bring not only their prior knowledge, learning dispositions, and passions, but an ever-expanding array of digital learning assets in which higher education is often struggling to keep pace with student expectations and abilities [20,21].

In this essay, we utilize a futures framework to consider what constitutes good learning and teaching now and how this might change in the future. It is a paper that focusses on several pieces of research that are highly regarded and which we have found useful in informing our view of the future learning and teaching in higher education. Thus, it is an evidence-informed reflection, rather than a comprehensive review of the area, and designed to stimulate readers to develop their own views about the future of learning and teaching in higher education. Specifically, this essay has sections that include futures thinking using a conceptual framework previously developed by the authors; six evidence-informed best teaching practices in higher education developed from four important and influential papers and reviews; and a final main section in which next practices in teaching and learning are proposed. A small conclusion provides a brief re-capture of the essay.

2. Future Universities

In thinking about the future, we refer to the futures model developed by Gurr [22,23] and shown in Figure 1. This draws on several ideas developed over time by Leithwood and Hannon. Over a decade ago, Leithwood [24] and Hannon [25,26] were involved in discussions about best and next practices in education, with Hannon and Mackay [27] taking this further by being more deliberate in thinking about future possibilities. Hannon [28] has continued to develop her futures thinking by considering outstanding schools that signpost more education trends.

This figure tries to connect the present (left circle) with the future (right circle). In the present, there are several considerations. In this figure, past practices are in the present circle but without an overlap with current and best practices. Often, past practices can influence what we do. Consider a conservative university, steeped in history and tradition. Changing parts of the university can be difficult because the university may always be looking back. This is not necessarily a problem if there is a deliberate engagement with the past that is neither a romanticized, nor overly critical, reflection. This can result in a genuine attempt to understand why things were done, what impact they had, and, perhaps, why these practices no longer exist. In terms of quality and equity considerations, this can reveal a history that may have had little concern for quality (which is a relatively recent phenomenon in education) and which had practices that would today be considered equitable and exclusionary.

Current practice is, of course, a description of what currently occurs. The problem is that often this is not well understood. How well do we know our universities and what students truly experience, and especially what we guarantee them compared with what is delivered?

Then, there are practices that are a clear break from what is currently known or practiced—next practices. They might be practices that are genuinely new—perhaps a disruption such as the construction of bitcoin in 2008 by the person or group called Satoshi Nakamoto or Dick Fosberry’s revolutionary high jump style (the Fosberry Flop) that saw him win the high jump gold medal at the 1968 Olympics. Or they might be an innovative reconfiguring of existing ideas and practices to create something new—for example, how Uber and Airbnb both used existing ideas and technologies to disrupt set industries.

Whilst these examples are of unplanned disruptions that led to major change, we can also be more deliberate in thinking about next practices so the future scenario planning strategies of possible, plausible, probable, and preferred futures can be used to predict and/or prepare for next practices [27]. To what extent do you personally and within your work within a university spend time thinking about the future? We do not think you need to concern yourself with 50-year predictions as things can become too fuzzy to predict well—although if you are currently engaged in constructing a new building, it will likely have a life-span of at least 50 years and how can you ensure it will be fit for purpose over all these years? However, a 20-year time frame is entirely reasonable and, we believe, essential for all educational leaders.

For the rest of the paper, we focus on exploring best and next practices in relation to student learning in higher education.

3. Best Practices in Learning and Teaching

Teaching in higher education is an honourable enterprise which has the power to transform the lives of students. Therefore, it is a responsibility to understand what constitutes good teaching in the service of optimising student learning [29,30,31]. Some of the challenges and opportunities in relation to enhancing learning in higher education institutions is in providing responsive learning and teaching experiences for increasingly diverse student populations. Beyond cultural, linguistic, and social knowledge and resources, our students are increasingly equipped with digital assets that were not part of the traditional landscape of teaching less than a decade ago [32]. More recently, this digital adeptness has been even more strongly surfaced through access to a range of AI information outputs [18,19]. These factors cause us to reconsider two functionally inseparable questions: What does good teaching look like and how can we promote deep learning in increasingly machine-enabled learning environments?

In education, the term ‘best practice’ can be problematic as there can be many conceptions of what this entails [33,34]. Shulman’s [35] view is that each discipline has a ‘signature pedagogy’, inherently distinct characteristics within an area of study which demand specific practices. While it is clear that signature pedagogies are a feature of higher education as ‘tertiary education aims at equipping students with advanced knowledge in a specific content domain…’ [36] (p. 567), there is an increasing body of research literature which seeks to investigate high-impact practices which seem to lead to better student achievement or learning engagement than others irrespective of the disciplinary domains [33,36,37,38,39].

Alongside this, our growing understanding of how people learn is shaping our understanding of what constitutes good practice. For example, Bain [40] advises that we need to continuously check for our own assumptions as educators. For example, the belief that all students are automatically primed and ready for learning in higher education is challenged by Perry [41], whose study of undergraduate students demonstrated that not all students in higher education are uniformly ready for the abstract and higher-order thinking characteristics of higher education settings. He generated a learning continuum which reflects various learning stages commencing with ‘received knowers’, students who adopt a banking view of knowledge, through to ‘commitment’, the most desired phase, the point at which students are independent and critical thinkers. Furthermore, he showed that students can be at a different stage of development in different subjects simultaneously.

Our understanding of good teaching has further complexity through a raft of approaches advocated in higher education. Some have research evidence such as case-based learning, research-based learning, service learning, public scholarship, technology-enhanced learning, learning by making and doing [42]. Beyond these, a new wave of scholars and thought leaders are proposing less well investigated approaches such as the list of 10 practices identified by Kukulska-Hulme et al. [43]: pedagogies using AI tools; metaverse for education; multi-modal pedagogy; seeing yourself in the curriculum; pedagogy of care in digitally mediated settings; podcast as pedagogy; challenge-based learning; entrepreneurial education; relational pedagogies; and, entangled pedagogies of learning spaces. To make sense of this, there is a need to focus on evidence-informed practices, not to constrain teaching innovation, but to align it with robust, validated, and reputable research that has been shown to be impactful [44,45]. Beyond this, educators need to understand not only the ‘what’ of teaching but also the ‘why’. That is, they need to understand the rationale that underpins these practices [33,46]. This paper seeks to support educators in making sense of the complexity of good teaching practices, not as a prescribed recipe but as an evidence-informed menu that educators can reliably call upon and then can contextualise to meet the needs of their discipline and improve student learning.

To unravel the literature about teaching mechanisms and conditions that enable deep learning, for this paper, we have decided to focus on what we consider to be four sources of information that are important for understanding good higher education teaching. The publications are Chickering and Gamson [47]; Ramsden [48]; Bain [40]; and Smith and Baik [39].

The paper by Chickering and Gamson [47] is a small four-page paper, published in the American Association for Higher Education’s Bulletin in March 1987. In the paper, they generate seven claims about good practice in undergraduate education:

• Encourages contacts between students and faculty.
• Develops reciprocity and cooperation among students.
• Uses active learning techniques.
• Gives prompt feedback
• Emphasizes time on task
• Communicates high expectations.
• Respects diverse talents and ways of learning.

Chickering and Gamson [47] (p. 2)

Whilst it is reported to be based on 50 years of research, it is not clear what the evidence base is and references are not used within the paper, although the reference list cites 42 publications. We can only assume as to the comprehensiveness of the coverage of evidence. So, it is a paper in which we trust the authors conclusions and most likely do so on the basis of who the authors are and the quality of the publication. That it has been trusted is evident by the 11,238 citations listed on Google Scholar.

Whilst there have been other reviews since Chickering and Gamson’s [47] publication, we wanted a very recent review paper and chose Smith and Baik’s [39] best evidence synthesis to provide current knowledge and to provide a sense of how the knowledge base has developed over time. This paper is emerging in terms of influence as it only has 38 citations so far on Google scholar. Nevertheless, we believe it will become influential over time because of the quality of the type of review process used. This review was restricted to publications within the Web of Science reference database, and so we know immediately that the range of publications has been delimited. Within this database, they detailed their search and inclusion/exclusion strategies to arrive at 78 papers that reported on 96 studies. Inclusion was based on:

• Focus on teaching and learning practice and appraisal/evaluation of the practice.
• Impact on either student learning or student experience.
• Generation of an effect size (through comparison, estimation, or correlation of impact)

They then weighted the paper for quality based on four criteria: population generalisability rating, measurement validity, ecological validity, and logic of inquiry clarity. Combining effect sizes and quality criteria, Smith and Baik [39] (pp. 1704–1705) summarized generally applicable and highly effective practices as those that:

• Provision well-structured representations of disciplinary knowledge/concepts in well-structured and clearly planned subject/program contexts;
• Are intellectually challenging;
• Take into account students’ goals and make clear the relevance of what they are learning;
• Deploy expert teachers who build rapport with students;
• Facilitate application/practice opportunities in authentic or simulated practice situations and give students opportunities to engage in inductive/exploratory/dialogic learning;
• Give students opportunities to interact and work with peers;
• Take into account students’ own role and agency in their learning, encouraging them to engage the meta-cognitive processes that ensure long-term encoding and retrieval, and which develop meta-cognition skills that underpin self-assessment, self-monitoring and management of their own learning.

These observations were summarized across nine categories that included clarity; inquiry; application; experience; challenge; relevance; interactions and relationship consolidation; and self-regulation. Compared to Chickering and Gamson’s [47] seven practices, there is an expansion of active learning to include inquiry, application, experience, and relevance (see the discussion below).

We also wanted to include some research that would give a more fine-grained view of student and teacher experience. Ramsden [48] draws upon earlier survey-based student experience research [49] to explore student perceptions of good teaching and learning to arrive at six principles of effective teaching and learning. It is a highly regarded publication with 14,607 citations on Google Scholar. Ramsden [48] (pp. 93–98) listed these as:

• Interest and explanation;
• Concern and respect for students and student learning;
• Appropriate assessment and feedback;
• Clear goals and intellectual challenge;
• Independence, control, and engagement;
• Learning from students.

While Ramsden’s work builds awareness of the student experience, Bain’s [40] research maps backward from the teaching methods, philosophies, and practices that set exceptional educators apart and which contributed to their effectiveness in the college classroom. It has been an influential book, with a total of 6840 citations across English and Spanish versions. His qualitative research methodology incorporated identification of 63 candidates who were perceived both by their colleagues and students as being ‘effective’. The findings that emerged were in response to drawing on a combination of interviews, case-studies, reflective practice, classroom observation, and artefact collection. Bain’s [40] reflections outline the complexity of teaching and learning, and the following is our attempt to summarise key ideas from this rich body of work, about what the ‘Best College Teachers Do’. They:

• Focus on learning goals.
• Create an engaging learning environment.
• Understand the importance of prior knowledge and perspective.
• Empathise and build relationships.
• Challenge and support students.
• Promote autonomy and critical thinking.
• Provide timely feedback and assessment for learning.
• Demonstrate a passion for teaching and subject matter.

The findings from the four papers are summarised in

Table 1. Summary from the four studies.

Chickering and Gamson 1987 [47] 7 Principles of Good Teaching	Ramsden 2003 [48] Key Principles of Effective Teaching	Bain 2004 [40] “What the Best College Teachers Do”	Smith and Baik 2021 [39] High Impact Practices
Encourages contacts between students and faculty Develops reciprocity and cooperation among students Uses active learning techniques Gives prompt feedback Emphasizes time on task Communicates high expectations Respects diverse talents and ways of learning	Interest and explanation Concern and respect for students and student learning Appropriate assessment and feedback Clear goals and intellectual challenge Independence, control and engagement Learning from students	Focus on learning goals Create an engaging learning environment Understand the importance of prior knowledge and perspective Empathise and build relationships Challenge and support students Promote autonomy and critical thinking Provide timely feedback and assessment for learning Demonstrated a passion for teaching and subject matter	Clarity Inquiry Application Experience Challenge Relevance Interaction and relationships Consolidation Self-Regulation

.

From these studies there seem to be six overarching trustworthy ideas about evidence-informed teaching practices. These practices are colour-coded in Table 1 as follows to show the overlap across the studies:

• Challenge: orange
• Clarity: red
• Activating Learning/Active Learning: green
• Learning Interactions/Learning Relationships: blue
• Self-Regulation: purple
• Feedback: brown

Please note that the black and italicised words denote a dimension that is unique to a particular study with no representation in the other studies.

Detailed descriptions of each are included below. The italicised script identifies a practice that was uniquely featured within a particular study.

3.1. Challenge

Challenge is about having high expectations of student learning, coupled with the provision of demanding content and student-centred teaching practices. This is clearly articulated in all four research studies, albeit with slightly different terminology or explanations. Chickering and Gamson [47] refer to having ‘high expectations of students’, Smith and Baik [39] the provision of ‘stimulating work’. Whereas Bain’s [40] ideas are interpreted as ensuring a balance of challenge and support, Ramsden [48] refers to the promotion of deep learning. Ramsden [48] notes that there is a challenge in moving students from surface to deep learning. Achievement of deep learning is helped by encouraging students to grapple with substantive concepts, engage in critical thinking and problem solving, exposing them to diverse perspectives.

These ideas are well supported by Ambrose et al. [50], who synthesised information from cognitive, developmental, and social psychology to explore how learning works in higher education. They emphasised that when teachers foster student curiosity, encourage exploration and present intellectually demanding tasks, they promote deeper understanding and greater motivation. However, they caution that it is not merely about making the work challenging but in ‘setting challenging but attainable goals’ [50] (p. 85). Furthermore, they recommended that identifying starting points for teaching requires understanding the prior knowledge of learners. Bain [40] considers this at length in his 2004 publication.

The challenge for educators in higher education is how prior knowledge can be efficiently determined in large classes or classes where it is not possible to engage with students regularly. At this time, learning analytics have some scope for garnering some of these data, but more complete solutions need to be found.

Despite this ‘challenge’ it, is an important practice. Hattie’s [51] research synthesis, although not focussing exclusively on higher education, affirms its importance and badges it as being a central feature of teacher expectations. Teachers signal high challenge/expectation through both their beliefs and actions that all students have the capacity to learn. This becomes evident to the students in how the teachers tap into their prior knowledge, the way they explicitly describe what success looks like, the provision of engaging learning, effective use of questioning, and effective feedback. Collectively these are interpreted as trust in student’s capacity, which in turn motivates students and encourages students to invest more in their learning [51].

3.2. Clarity

Clarity is focussed on explicitly naming what learning outcomes are expected at the lesson level, course level, and then throughout course in classroom discussions as connections are continuously made between new and old learning. Three of the studies overtly identify clarity as being significant. While not noted in the Chickering and Gamson [47], it could be broadly conceived of as falling under their description of ‘communicates high expectations’ and was consequently added to the list of six evidence-informed practices. Ramsden’s work frames clarity as stimulating learner ‘interest and explanation’ and the provision of ‘clear goals’ suggesting that good teaching begins with eliciting curiosity and clearly articulating teaching foci. Hattie’s [51] meta-analysis endorsed the importance of communicating ‘learning intentions’ as a way of spotlighting the important learning that is to be addressed. Schneider and Preckel’s [36] systematic literature review of 38 meta-analyses situated firmly in higher education affords ‘clarity and understandability’ with an effect size of 1.35. As with all meta-analyses, it is unhelpful to merely assign a number to student learning, but to explicate what teaching practices occurred in concert with one another to help us gain a sense of what this looks like in action. Roska et al. [52] state that when work is well organized, students perceive their teachers to be more invested in their learning, which in turn builds their confidence in both their teacher’s expertise and in their ability to navigate learning content. Bain’s [40] work helps us to gain some insight into the lived experience of what practitioners do to achieve clarity. He notes that the effective teachers in his study were discerning in identifying what is critical and or important to learn and added to this by saying that they were expert at anticipating what would be difficult to learn. In addition to this, they respectfully challenged the students’ prior assumptions or misconceptions, designed learning sequences to support understanding, and then introduced information in small bites. The last dimension about small chunks of information is well supported by Ambrose et al. [50] and reflects Sweller’s [53] view that our cognitive architecture has limited capacity for processing vast amounts of information, and, consequently, we should avoid cognitively overloading students. Given the very abstract and conceptually demanding learning required of students in higher education, this is even more important if we want students to be able to integrate multiple sources of information into their long-term memories, leading to more robust conceptual schemas.

Smith and Baik [39] expand on this student level view by noting that structuring of knowledge needs to be evident not only at the class level but within the curriculum design of the course and, furthermore, delivered by experts. The importance of curriculum alignment at the course level has been strongly underscored by Biggs and Tang [54]. However, the relationship between clarity and experts delivering content in higher education was not discussed in any of the other studies and needs further investigation. One plausible explanation emerges if we apply the Dreyfus model of skill acquisition to this scenario. Dreyfus and Dreyfus [55] describe skill acquisition moving from novice to expert, and they provide descriptors of behaviour at each level. Novice teachers, with their developing knowledge and experience, may need to be more rules based and mechanistic in how they approach their teaching, whilst expert academics may draw on their authoritative scholarship and experience to respond to student concerns more intuitively and effortlessly.

3.3. Activating Learning (Active Learning)

Active learning shifts responsibility to the students as they move from being passive recipients to being ‘active’ participants in making their own meaning [36,56,57]. Pedagogical practices typically involve a combination of activities such as listening, thinking, creating, discussing, problem solving, and reflecting. All four studies highlighted practices which could be described as active learning. However, when their work was analysed in more depth, a more accurate term capturing this suite of practices would be ‘activating learning’. These descriptors are consistent with Kolb’s [58] view of experiential learning, which features the importance of students engaging in reflection. Through the intentional provision of time for sustained reflection, students are able to transform their understandings, leading to new or revised thinking. Ramsden [48] and Bain [40] affirm this, then add the important aspect of teachers designing learning experiences where students test these revised propositions. Furthermore, the discussion in all four studies provides a picture of behaviours which go beyond the mere provision of activities and which suggest how these activities are intentionally designed to be intellectually stimulating and to promote higher-order thinking, which in turn enhances student learning engagement. Ramsden’s [48] research specifically refers to the notion of ‘engagement’. It is important to understand that in the more recent higher education literature, scholars generally define engagement as having an action orientation which encompasses active participation in academic and extracurricular activities [59]. For this paper, engagement in the classroom is conceived of as ‘engaging students responsibly and dynamically with the subject matter’ [48] (p. 146). This view is complemented by Smith and Baik [39], who outline active learning as comprising inquiry, application, and experience. They catalogue the following teaching methods as enabling active learning: problem-based learning, case-based learning, and inquiry-based learning. Although these three methodologies have different orientations, they are similar in that they all involve some level of ‘questioning, problem solving, investigating and testing of ideas’ [39] (p. 1705).

The important new conceptualisation that Smith and Baik [39] surface is that of episodic richness, which concerns authentic opportunities to stimulate deep cognitive thinking through applying concepts which mirror real-world purposes. Bain’s [48] earlier research endorses this view stating that in his study the ‘best teachers’ facilitated learning conversations to authentically reflect relevant and purposeful complex problems emerging from their discipline. In his view, this activated thinking like a professional within the discipline.

Questioning techniques, specifically the nature of questions and the consequent thinking generated, are highlighted as being critical in activating learning and are noted in the Smith and Baik [39] work and explored in depth by Bain [48].

One further area noted by Smith and Baik [39] was that of consolidation, and this included activities which required retrieval practice and revision. Although classified in their work as one of their nine high-impact strategies, the provision of these could be said to fall under the banner of activating learning as it is an element of meta-cognition. Drawing on his own work and that of cognitive psychology, Lang [60] puts forward a strong case for designing learning tasks which focus on memory retrieval through practise. His claim is that each discipline has a body of knowledge which students must know. Through multiple exposures with this foundational material, students are strengthening their neural pathways, which, in turn, embeds this into the long-term memory. Once stored in long-term memory, students can more efficiently retrieve information.

3.4. Learning Relationships

The very heart of learning relationships is about mutual regard and rapport for all the learners in a class. It has three very important dimensions. First, it is about establishing respectful and welcoming educational partnerships both with the teacher and with students. The teacher is central to establishing learning environments which are conducive for experimentation, risk taking, and seeking clarification. The network of learning relationships in a class is further increased through the teacher establishing learning norms and creating learning and teaching experiences, which builds a sense of belonging to a learning community. This is specifically achieved through harnessing the inherently diverse learning skills and dispositions of the students themselves. Finally, student diversity is regarded as a form of social and intellectual capital. Whatever learning repertoire, challenges and/or dispositions the students uniquely bring are valued, and these multiple perspectives are exploited to improve the learning of the group. Chickering and Gamson [47], Ramsden [48], and Bain [40] mirror this view.

Bain [40] expands on these and provides an additional unique insight. Teachers in his study nurtured a sense of mutual obligation for the learning process and the consequent outcomes through establishing a social compact. For example, in his study, he observed teachers explicitly pledging their commitment to providing the best learning and teaching possible whilst at the same time sending respectful yet demanding invitations which communicated that students have a role to play in determining their success as learners.

Beyond this, Bain [40] describes a suite of other behaviours that built learning relationships such as being responsive to individual differences, recognising prior knowledge, identifying student misconceptions, along with making adjustments either in their programs or the teaching strategies they adopted. In Bain’s [40] view, these behaviours build the trust necessary for learning to take place. This very closely resembles Ramsden’s [48] sixth principle of effective teaching and learning: learning from the student. In essence, this about the teacher being an excellent diagnostician of learning and responding to student misconceptions through adjusting learning experiences, the curriculum, and the assessment. These observations are consistent with the research on how people learn in higher education and the importance of cooperative class climates [50] and positive teacher and student relationships [44]

Bain [40] offers two further observations that sit under the learning relationships category and which emerged from his work. First, he notes the academics in his study were willing to expose their vulnerabilities as learners. Specifically, they intentionally communicated with students about how they may have struggled with understanding concepts in their own learning. Although Bain concludes that these behaviours collectively build a sense of rapport and empathy with the students, when examined through the lens of cognitive psychology, some crucial aspects of self-regulatory learning were being modelled to students. In fact, the academics were modelling what expert learners do as described by Bransford Brown and Cocking [61]. They were demonstrating self-monitoring behaviours such as noticing discrepancies; being comfortable with the ambiguity of not knowing; and persistence when faced with learning roadblocks.

Bain [40] and Ramsden [48] both suggest that another way to build rapport with students in higher education is by teachers sharing their sense of curiosity, wonder, and passion for their discipline with the students. Although not noted by Chickering and Gamson [47] or Smith and Baik [39], it is an interesting idea which is worthy of further investigation, and it may be related to modelling effective learning behaviours.

Although all four studies noted some form of peer interaction as being crucial, they did not fully elaborate on these in sufficient depth to explore fully in this paper. Whilst Smith and Baik [39] provided collaborative learning as an example of social learning, how this was understood and how this could be effectively operationalised was not discussed. It is possible to surmise that learning with and from peers is important, and beyond the four studies examined, theorists such as Light, Cox, and Calkins [62] advocate that through social construction of knowledge, learners’ understanding is deepened. Furthermore, Race [63] regards working in small groups as one of the factors that underpins successful learning. Whilst Kilgo et al.’s [37] longitudinal study of ten high-impact teaching strategies endorsed by the Association of American Colleges and Universities (AAC&U) highlighted that of the ten strategies nominated, active and collaborative learning had significant positive impact within liberal arts courses.

3.5. Self-Regulation

Self-regulation is the process of monitoring, controlling and adjusting ones cognitive, meta-cognitive, and behavioural learning strategies. Self-regulation emerged as a significant theme in the three more recent studies [39,40,48]. The need for students to build their awareness as learners in order to chart a course of action to plan, monitor, and adapt strategies has evolved as our understanding of how people learn has evolved. Ramsden [48] refers to the importance of building student independence and control. He explains that this requires that students are empowered with cognitive capabilities such as synthesising, analysing, critical thinking, and dispositions such as creativity, persistence, and reflexivity. Bain [40] aptures this same sentiment and describes this as teachers explicitly building autonomy and critical thinking into their teaching practices. He reflects that educators in higher education should consider adopting a more nuanced understanding of critical thinking and refers to Arons’ [64] ten reasoning abilities and habits of thought to demonstrate this. These include consciously raising questions, being explicitly aware of gaps in available information, discriminating between observation and inference, recognising that words are symbols for ideas, probing assumptions, drawing inferences, performing hypothetical–deductive reasoning, discriminating between inductive and deductive reasoning, testing one’s own line of reasoning, and developing self-conscious concern in one’s own thinking and reasoning. Whether one agrees or disagrees with these 10 propositions, reasoning abilities play an important role in developing capabilities necessary for the types self-directed learning necessary for developing deep understanding necessary for higher education and for life. Smith and Baik [39] list self-regulation as one of their nine high-impact strategies. The descriptions provided in the studies they reviewed support the facilitating practices of educators, which build both learning independence and efficacy. This was achieved through the provision of learning which supported both meta-cognition (internal cognitive processes related to reflection, being self-aware, self-monitoring) and self-regulation (external behaviours such goal setting, time management, persistence, monitoring, emotional regulation, and adjusting behaviours).

In the face of a future where students will increasingly be asked to respond to complex problems for which there may not be clear answers, being equipped with a combination of meta-cognitive and self-directed learning capabilities is crucial.

3.6. Feedback

Feedback is information which improves learning [65]. Effective feedback involves the use of learning prompts which can address errors and/or misconceptions in students existing understanding or provide guidance on actions students could take to improve their work. The latter component is often referred to as feedforward as this input has the capacity to be transferred to a future learning context [66,67]. Three of the reviewed studies—[40,47,48]—underscore the importance of feedback, and it is somewhat perplexing that feedback was not listed as one of Smith and Baik’s [39] high-impact practices, but in revisiting their inclusion criteria, their focus was on the impact of teaching and learning practice, rather than how students achieved learning outcomes (which would have included feedback). Despite this, we believe that feedback should remain on the list of six evidence-informed practices as this is supported by contemporary scholars in the field who are examining the dimensions of effective feedback in higher education. For example, a literature review conducted by Jackel et al. [68] for the Higher Education Academy (HEA) exposes some of the difficulties of deconstructing what constitutes effective feedback as applied to higher education. A significant difficulty is that ‘while the pedagogical theories behind the current consensus on feedback are sound, solid evidence for effectiveness is correspondingly thin’ [68] (p. 25). Despite the need for more research in this area, one of the overarching themes in the literature is that good feedback is learner centred [69] and engages students in dialogues which address three dimensions: sense-making, agency, and impact. Sense-making is about ensuring that feedback is designed in such a way that it is clearly understood [70]. Agency is about increasingly shifting the locus of control from the teacher to the learner. In well-designed learning environments, students develop the capacity of students to actively seek feedback from a range of sources (teachers, peers) as it mirrors real-world purposes, and then to evaluate the usefulness of the feedback [70,71]. Through exposure to these feedback models, students increasingly gain confidence and skill in appraising the quality of their own work and that of their peers [72]. Finally, feedback impact is shown through improvement in student learning—cognitively, meta-cognitively, and/or relationally [65].

4. Next Practices in Student Learning

In this final section, we consider ideas about next practices in teaching and learning, and we accomplish this in two ways. Firstly, by considering the six evidence-informed teaching practices, and secondly, by considering possible, plausible, probable, and preferred teaching scenarios.

4.1. Next Practice in the Six Evidence-Informed Teaching Practices

4.1.1. Challenge Next Practice: An Increasing Emphasis on Ethical ‘Wicked’ World Problems

Along with the descriptors of challenge provided in the best-practice section, going forward there needs to be a stronger focus on the curriculum that deals in matters of ethical reasoning and critical thinking, where the lines between human and machine become blurred [73,74]. For example, questions like these need to be considered: What are the moral obligations of nations who have more resources? How do we manage inequities in relation to food, water, and even digital access? What is academic integrity? How do we better manage global citizenship responsibilities specifically in relation to global warming and sustainability?

4.1.2. Clarity Next Practice: Strengthening a Focus on Macro-Level Learning Pathways

Determining what is valuable to ‘know, do and be’ in an AI-pervasive world within a course still requires some oversight by human disciplinary experts. Designing courses which allow for personalised automated learning experiences, along with a vision of how these are connected, needs to be orchestrated with a human focus and going forward these may increasingly be groups of international experts exposing students to multiple and diverse perspectives.

4.1.3. Activating Learning Next Practice: Ensuring the Focus Remains on Deep Learning

There are currently many applications being created which can provide practise and memory retrieval, and, as Lang [60] notes, this is crucial in having a strong foundational knowledge to draw upon. However, we need to ensure that we are not distracted by the next ‘shiny toy,’ which may keep students busy with surface-level learning, and instead keep focussing on learning which is transformative and purposeful. It is easy to get distracted by pedagogies such as, for example, learning with robots or learning with drones [75], which have limited scope for conceptual transference. It is important to remain faithful to the thinking of Biggs and Tang [54], who ask educators to begin with the learning outcomes in mind, then to determine how this will be achieved.

4.1.4. Learning Relationships Next Practice: Amplifying Social Emotional Dimensions of Learning

In addition to the practices focussing on learning with and from others, as we draw on technology more ubiquitously, the OECD [76] (p. 1) argues that our diverse educational institutions and workplaces will require teaching that focuses on developing ‘…empathy and respect for others…’ There is a significant body of research on the importance of engagement and belonging in the higher education sector [77,78,79,80]; however, it may need to be featured even more prominently in a range of formal and informal infrastructures, both within classrooms and support services. The Horizon Report: Teaching and Learning Edition [81], which provides perceptions of technology impact on higher education teaching and learning, echoes these strong sentiments stating that we need to ‘support(ing) students’ sense of belonging and connectedness’ (p. 29), and the report provides examples of how colleges in the United States are grappling with this. While belonging is considered crucial, what constitutes ‘belonging’ in digitally mediated environments has not been fully explored. For example, Birdwell [82] invites readers to reconceive the notion of learning spaces which incorporate the home as part of the physical and digital ecosystems, yet what interventions are required by the educators to create a learning community is not fully explored. It may be that belonging is achieved organically in digitally mediated environments as students move seamlessly in an out of various social platforms, yet what actions are needed to create a genuine sense of cohesion and supportive risk taking as conceived of in the best practice section needs further investigation. As we learned during the move to remote teaching during the pandemic, it will be important to monitor this, to maintain an ongoing dialogue with faculty and students, and provide options and opportunities to invite students to engage with one another, to learn how to negotiate, hear feedback, cope with divergent views respectfully, collaborate, and disagree. The importance of some form of social learning cannot be understated. The Innovating Pedagogy 2023 report [43] invited a group of academics and researchers to offer their views on possible pedagogies for an interactive world. Out of the ten pedagogies proposed, two of the pedagogies had a humane, social, and collaborative orientation: ‘pedagogy of care in digitally mediated settings’ [43] (p. 28) and ‘relational pedagogies: working in and across disciplinary and professional boundaries’ [43] (p. 45).

4.1.5. Self-Regulation Next Practice: Amplifying Self-Directed Learning Capabilities

The world is a dynamically shifting place, and we cannot predict everything our students need to be equipped with; however, this evidence-informed practice in some ways needs to be elevated above all the others. Having this set of capacities allows us to harness a range of high-order thinking and problem solving across contexts, historical, economic or political, or digital terrains. Students of the future need to be coached/taught even more explicitly, in the words of futurist Tofler [83] (p. 477) to ‘…learn, unlearn and relearn.’ Mandernach [84] (p. 45) puts it eloquently when she says students, ‘…need to navigate content, ask questions, consider contextual factors, and apply knowledge in meaningful ways.’ The implications of this will have impacts on our educators, who also may need support understanding how self-regulation, meta-cognition, and reflection can be deployed within their disciplines.

4.1.6. Feedback: Next Practice: Provision of Feedback in Multi-Modal Forms

As well as the need for more empirical research about the impacts of feedback in higher education on student achievement, as described in the best-practice section of this paper, there needs to be an investigation about how feedback is effectively designed and ‘distributed’ rather than delivered in multi-modal environments. There are already some preliminary and tentative explorations regarding this. For example, Ryan’s [69] discussion paper considers the operational elements of effective feedback in digital learning environments, whilst Santos and Henriques [85] propose that we should examine how learning management systems can be used to yield more specific information about learner engagement.

Theorists such as Carless et al. [71] and Price et al. [86] do not focus on the modality of delivery but rather centre their reflections on the dialogic process of feedback. In this, feedback emerges from a two-way interaction between the giver and the receiver of the feedback, who may or may not be the teacher. They believe that it is through this participatory dialogue that the learner is better enabled to develop deeper insights and understanding. If this view is correct, there is scope for exploring what the implications for both educators and students might be in the ever-developing digital learning environments.

4.2. Possible, Plausible, Probable, and Preferred Teaching Scenarios

The future is an unknown destination, yet, as identified earlier in this paper, we can begin to hypothesise what next practice might look like through the use of the four future scenario planning strategies from Figure 1 of possible, plausible, probable, and preferred. In order to gain clarity and maintain the focus of each scenario, the following definitions were established as discussion guard rails.

• Possible futures are those things which operate outside the bounds of certainty.
• Plausible futures are those things that can eventuate within our existing knowledge bases.
• Probabilities futures are eventualities that are supported by strong evidence.
• Preferred futures are about the creation a futures focussed education that is aspirational.

4.2.1. Possible Scenario—Substitution

In this scenario, higher education becomes increasingly redundant as information generation and teaching become uncoupled from one another. In a think piece, Minocha [87] (np) quotes Indian Philosopher Jiddu Krishnamurti:

‘What if… there is no teacher, no pupil; there is no leader; there is no guru; there is no Master, no Saviour. You, yourself, are the teacher and the pupil; you are the Maser; you are the guru; you are the leader; you are everything.’

This agentic view of higher education initially can seem quite appealing and to a certain extent conforms with Knowles’ [88] seminal work on self-directed learning. Anecdotally, as teachers both teaching online during the pandemic, it was clear some students did successfully pursue their own passion projects, maintain their own learning progress, and achieve expertise within their chosen domain; however, not all is as it seems. According to Kukulska-Hulme et. al. [43], teacher mentors or guides will still be required to help students develop the capacity to critique and verify so they can make sense of the hallucinations, inherent biases, and incomplete or absent voices generated in AI outputs. Additionally, if we believe that one of the aims of higher education is to empower students to become experts in their field, then the possible substitution scenario becomes problematic. Ericsson and Poole’s [89] research found that what sets experts apart is that they engage in deliberate practice which is overseen by a coach or teacher familiar with their field, which involved goal setting feedback and which added to their mental representations of their performance. AI can already mimic the role of a learning coach when given the right prompts; however, the body of knowledge being investigated may still need to be situated in a disciplinary or multi-disciplinary human context, which should include moral, social, and ethical dimensions of learning.

The Innovating Pedagogy 2023 report [43] suggested that education could happen in the metaverse, which consists of virtual worlds where people interact through avatars, and this could be considered as another version of this substitution scenario. The perceived advantages of learning in the metaverse were that students would have more personal freedom and a more interactive social communication platform. However, Kukulska-Hulme et al. [43] do not believe the metaverse has fulfilled its promise yet, because of many technical challenges and privacy issues.

4.2.2. Plausible Scenario—Extension of Existing Realities

In this scenario, best- and next-practice trends happening now continue and become current practice. Generative AI (GenAI) is an obvious example to illustrate this. For many, the emergence of ChatGPT in November 2022 was the first major awareness of the potential of GenAI. ChatGPT was built on OpenAI’s third-generation GPT, and there is already a fourth version available [90].

Sharples and Pérez y Pérez [91] believe that AI can be used creatively to augment existing learning and teaching practices and outline the following crucial roles that AI will play. The first is that AI plays the role of personal tutor, able to be responsive to individual learner queries and wondering. The second is that AI performs the function of collaborative coach working with individuals and or teams on research projects. The third is that of study buddy, adopting a more supportive role when students do not feel able to approach a teacher or a peer.

The inherent challenge with this scenario is that it seems that a teacher/educator still needs to have oversight of how individual and discrete bodies of knowledge are connected to ensure learning cohesive and purposeful learning.

4.2.3. Probable Scenario—Complementarity

The OECD [76] (p. 5) note that in relation to AI,

‘… human and artificial intelligence can complement each other and, as a consequence, what (that) new knowledge and skills must be acquired and cultivated. By creating AI systems that are able to learn in increasingly sophisticated ways, human intelligence also becomes more sophisticated.’

This view operates from a person-plus world view, where we can harness efficiencies created as a result of using AI, and where AI is enhancing what humans can do. The conversations begin with human problems or challenges and then the determination about what technology helps us to solve these. Thus, technology in this example is complementary to what humans can do. In terms of the six evidence-informed teaching and learning practices identified earlier in the paper, all remain vitally important in this scenario. However, self-regulation is even more prominently featured as higher capacity for creativity and imagination is demanded. This is further substantiated by Avissati et al. [92], who note the more highly sophisticated thinking required for innovation requires auditing both curriculum and pedagogical practices that forefront curiosity and the capacity to question existing ideas, along with the capacity for question-making. They believe these adaptive capacities better equip students for continuous adaptation in response to evolving technologies and consequent changes in what constitutes work and life.

4.2.4. Preferred Scenario—Transformation

The rational underpinning this final more transformational scenario emerges from a more humanistic positioning of higher education [13] ). It requires us to shift our thinking away from the technology alone as the end point and ask more profound questions about the type of learners we want leaving our institutions and how will their knowledge be used in the service in improving our human condition. This is captured in the 2021 UNESCO Report [93], stating that we should,

‘build a new social contract for education, grounded on principles of human rights, social justice, human dignity and cultural diversity. It unequivocally affirms education as a public endeavour and a common good.’ [93] (np)

What can be said with confidence is that the future of higher education will involve multiple technologies and modalities. Whilst new AI technology is rapidly impacting on higher education, Mandernach [84] cautions that we should not be mindlessly rushing to ‘integrate AI into learning experiences or enacting fundamental changes to instructional format… (but reflection on) what does it mean to teach and what does it mean to learn’ [84] (p. 45). We should consider what kinds of learners we want leaving our institutions. We need to equip them to have high levels of self-efficacy and adaptivity to navigate any social, technological, economic, environmental, and political scenarios they may encounter. The OECD Future of Education and Skills 2030 report [76], although published in 2019 well before the pandemic and the deluge of AI, forecasted that we should teach our young people to mobilise ‘cognitive and meta-cognitive, social, and emotional skills and physical and practical skills’ [76] (p. 1). By this view alone, the six evidence-informed best practices are supported, and as shown in the next practice considerations, they will continue to evolve.

5. Conclusions

In this essay, utilizing four important and influential papers and reviews on teaching and learning in higher education, we have described six evidence-informed best teaching practices:

• Challenge;
• Clarity;
• Activating Learning/Active Learning;
• Learning Interactions/Learning Relationships;
• Self-Regulation;
• Feedback.

These are important practices for those teaching in higher education to optimise the learning of students. As noted before, these are described to support educators in making sense of the complexity of good teaching practices. They are not presented as a prescribed recipe but rather an evidence-informed list that educators can reliably call upon and then can contextualise to meet the needs of their discipline so they can improve student learning. These practices will continue to develop, and at the end of the paper, we described examples of how they might evolve in next practices, and, in so doing, we showed that they are not static concepts or practices. We also provided four more broadly focussed, albeit small, scenarios for the ideas of the possible, plausible, probable, and preferred. Again, we are not suggesting certainty or prescription. This paper can act as source to stimulate practitioner and researcher thinking about the future of learning and teaching in higher education and to adopt a futures mindset to this thinking.