The literature reviewed explored the effects of the COVID-19 on higher education online programs. Ossiannilsson et al. (2015) conducted a global review of quality models in online education which revealed that before the COVID-19 pandemic, it was expected that by the year 2030 an estimated 414.2 million students would be in higher education programs or courses around the world, due in part to the accessibility of online education. Lewis (2021) evaluated the challenges in online higher education academic programs which concluded that many of the students estimated to enroll in online higher education programs were expected to be adult learners due to their life responsibilities. However, a study by UNESCO et al. (2021) with data from 143 countries focusing on education amid the COVID-19 pandemic revealed that 94% of countries had implemented some type of remote learning modality as a direct response to the COVID-19 pandemic, expediting the move to remote learning. Further research by Kucuk-Avci et al. (2022) on the effects of the COVID-19 pandemic on remote higher education argued that the shift to remote learning as a result of the COVID-19 pandemic was emergency remote teaching which is not the same as intentional distance or online education. Through a bibliometric analysis of the emerging research, they determined that online learning experienced a 60% growth in higher education after the pandemic. Kucuk-Avci et al. identified the purpose of emergency remote teaching as an effort to minimize physical space as opposed to online education where the focus is on minimizing psychological space while facilitating teaching and learning. Likewise, after conducting an evaluation on the differences between emergency remote learning and online teaching, Hodges et al., (2020) identified the goal of emergency remote teaching as a means of providing short-term access to instructional resources. They found evidence that effective online instruction needs to be planned and requires the development of an ecosystem for community building and providing learner support.

A systematic review of the literature on adult learning in online spaces by Abedini et al. (2021) showed increased educational opportunities for adult learners due to advancements in the digital era. Abedini et al. define adult learning as a knowledge making process that happens as a response to the experiences that an individual has been exposed to, thus making it a lifelong learning phenomenon. The literature reviewed discussed the differences in characteristics between child and adult learners and the theories that explain adult learning in a formal education setting. Amponsah (2020) conducted a qualitative case study with 21 adult students (between the ages of 27 and 54) to evaluate the learning styles of adult learners in higher education and concluded that the different learning styles of adult learners impacts their outcomes in online educational environments. Likewise, Williamson and Stevenson (2022) analyzed adult learning principles and determined that there are differences in the characteristics of adult learners. In addition, Abedini et al. led a comprehensive review of the literature on adult learning and adult learners in online learning communities and concluded that adult learners are self-directed and intrinsically motivated when learning life-relevant skills. Furthermore, considering the characteristics and needs of adult learners, Hajdu et al. (2022) analyzed the motivators of 27 adult learners through a motivated strategies for learning questionnaire. They proposed that an understanding of the factors that drive adult learners is essential for successful adult education programs.

The literature reviewed on adult learning theories showed an increased focus on andragogy as a prominent and influential adult learning theory. In 1978, Malcolm Knowles proposed andragogy as a learning theory to explain the needs of adult learners (Amponsah, 2020). Knowles (1980) proposed four basic principles for andragogy to define the adult learner:

1. Self-concept: adult learners are independent and self-directed
2. Experience-centered: adult experiences are fundamental for the construction of knowledge
3. Professional outcome: adult learners are driven by the purpose of the educational experience
4. Problem-centered: adult learners want to acquire practical and applicable skills

Knowles subsequently added a fifth principle, self-motivation, and expanding on adult learning theory (Abedini et al., 2021), Merriam (2008) synthesized consequent research on adult learners and proposed the inclusion of an additional assumption of the adult learner: goal-orientedness.

Although Knowles’s work was crucial in the conceptualization of adult learning, the literature reviewed discussed the opportunities afforded by andragogy as well as its limitations. For example, Merriam and Caffarella (1999) analyzed different models of adult learning and outlined andragogy’s scrutiny by researchers including the question of whether or not it qualifies as a theory and if so, is it a learning theory or a teaching theory. They also found that researchers strongly critique the isolated nature of the learner in andragogy, calling for awareness of their sociohistorical context. Similarly, Abedini et al.’s (2021) review of the literature on adult learning emphasized how andragogy does not account for the collaborative factors that might influence adult learners nor does it focus on learning itself, rather it focuses on the characteristics of the learners. Furthermore, Amponsah (2020) studied adult learning styles and proposed that they be accounted for in adult learning theories in addition to learner characteristics. Consequently, Williamson and Stevenson (2022) evaluated adult learning principles through an online boot camp course with nearly 300 participants which led to their critique of andragogy as having a restrictive focus on the learner and recommended that it expand to consider the implications of student-teacher interactions in their learning.

As previously stated, Abedini et al. (2021) and Williamson and Stevenson (2022) concluded that self-directed learning is a significant factor that drives adult learning in the online environment; however, research by Amponsah (2020) on adult learners in higher education suggests that adult learners have many different characteristics that should be accounted for in the design of online courses including problem-solving, curiosity, cautious observation, logical reasoning, and analytical traits. Amponsah and Abedini et al. recognize that with an increased accessibility to higher education in an online format, it is important to understand how adult learners navigate those courses and determine if change or an adaptation to the frameworks and theories is needed to make learning more accessible and suited for the learners. Henschke (2011) explored emerging research in the field and suggested that andragogy should evolve from Knowles’s original proposition, while proposing that analyses of adult learning should incorporate other adult learning theories.

In addition to the varying definitions for learning informed by the different theoretical frameworks as referenced previously, the literature reviewed discussed two types of learning: formal and informal. While conducting a study to validate non-formal and informal learning in higher education, Souto-Otero (2021) reviewed the literature on the different types of learning and established that formal learning is by design and occurs through education, which is composed of learning communities, a curriculum, and pedagogy. Furthermore, the literature reviewed showed agreement in the definition of formal learning. Carraro and Trinder (2021) explored language learner’s perceptions of online formal learning environments and adopted Stevens’s definition where formal learning is connected to a learning institution and course outcomes. Chatterjee and Parra (2022) examined formal and informal learning through a qualitative case study in a technology course and defined formal learning as goal oriented and structured with the objective of earning a grade. Similarly, through a study that explored learning motivation of adult learners, Hajdu et al., (2022) defined formal learning as a structured process conducive to some type of qualification. Likewise, Souto-Otero conducted a validation analysis of the different types of learning and established formal learning as intentional and a process aligned with defined learning objectives leading to a certification or degree. However, through an assessment of the characteristics of formal, informal, and non-formal learning, Johnson and Majewska (2022) acknowledged the disadvantage of formal education in its inability to accurately capture and describe what is happening in the learning environment or how the curriculum is being implemented by educators and received by learners.

In contrast, a review of the literature by Carraro and Trinder (2021) led to their determination that informal learning takes place outside of the structured learning activities that are planned for in the context of education, and students are not intentionally engaging with it. Research conducted by Chatterjee and Parra (2022) examining formal and informal learning through a qualitative case study in a technology course established the definition of informal learning as implicit and unplanned. Similarly, Souto-Otero (2021) defined informal learning as unintentional from the learner’s point of view, taking place anywhere at any moment throughout their daily life activities, and not structured around predetermined learning objectives. However, Johnson and Majewska (2022), through an evaluation of the characteristics of formal, informal, and non-formal learning, added to that definition that informal learning is context dependent and they raised doubts regarding its effectiveness for the acquisition of conceptual content. They discussed the implications of not having a set curriculum, claiming that it can lead to suboptimal learning outcomes in students.

Additionally, the literature reviewed discussed the emergence of a third form of learning emerging in literature, non-formal learning. Johnson and Majewska (2022) defined non-formal learning as a type of learning that results from the interaction of elements of formal and informal learning. Likewise, Souto-Otero (2021) defined non-formal learning as integrated in planned learning activities but not planned for in the learning outcomes, however they described it as intentional for the learner. The European Centre for the Development of Vocational Training (Cedefop) defines this type of learning as intentional from the student’s perspective but going outside the bounds of the planned learning activities of educational institutions (Souto-Otero, 2021). However, the literature reviewed determined that non-formal and informal learning are ubiquitous and more conducive to lifelong learning (Abedini et al., 2021; Carraro & Trinder, 2021; Hajdu et al., 2022). Johnson and Majewska developed a model illustrating the relationship between learning, education, and the curriculum and identifying where formal, non-formal, and informal learning fall between them (see Figure 2.1 below). For the purpose of this literature review, we focus on adult learning and learning theories in a formal higher education setting.

Figure 2.1

Formal, Informal, and Non-Formal Learning Model

Formal, Informal, and Non-Formal Learning Model. Johnson and Majewska (2022)

In the literature reviewed on learning theories, the discussion focused on three theories: behaviorism, cognitivism, and constructivism. In addition, through a synthesis of learning theories, Schofield (2019) identified and discussed nativism as an important learning theory. Schofield’s review of the literature synthesized nativism as a theory that proposes that learning is an innate phenomenon in human beings and intelligence is biologically predetermined. Yet, Schofield argued that nativism rejects the effects that context and the environment might have on knowledge and skill acquisition, thus reducing the transformative power of education. This literature review focuses on discussing behaviorism, cognitivism, and constructivism. Schofield described the focus of behaviorism on the individual’s exposure to different stimuli, while Picciano’s (2017) discussion of theoretical frameworks added that behaviorism in learning focuses on observable actions, paying particular attention to the student’s response to external stimuli. Clark (2018a) synthesized the literature focused on behaviorism and further added that for educational behaviorists, learning takes place when target behaviors are acquired. Furthermore, Bélanger (2011) concluded that “at a pedagogical level, behaviourists emphasize the importance of establishing gradual operational learning objectives, from simple to complex tasks, with a view to the predicted set of behavioural outcomes” (p. 19). Yet, Schofield argued that psychological functions are not accounted for in education through the behaviorist approach, thus the acquisition and replication or lack of behaviors are the only measure of learning.

Subsequently, cognitivism emerged from psychologists Jean Piaget and Robert Gagné as they recognized the importance of cognition in learning and proposed the stages of cognitive development and the conditions of learning respectively, which implied that learning goes beyond observable behaviors (Clark, 2018b). However, after a review and synthesis of the literature, Clark proposed that cognitivism kept education teacher-centered by requiring teachers to design instructional opportunities that promote the conditions of learning and guide students through the stages of cognitive development. Schofield (2019) reviewed the literature on learning theories and concluded that constructivism emerged following cognitivism, and it is thought to have derived from Piaget’s stages of cognitive development, which proposes that learning is contextual and that learners create meaning and knowledge by making connections to their context and their environment. Clark (2018c) observed that this claim is supported by Vygotsky’s social development theory and the zone of proximal development, which place the responsibility of learning the knowledge making process on the learner as opposed to the teacher. However, Siemens (2004) openly questioned the limitations of learning theories in the digital era when knowledge acquisition is not linear. Taking into account the existing learning theories, Anderson (2011) conducted an analysis of the affordances of the Web in online education and suggested that with the increasing changes of the Web it is too soon to construct a theory for online learning. Nonetheless, the literature reviewed discusses theories and frameworks developed for online education.

A theory for online or distance education is transactional distance developed by Dr. Michael G. Moore in the 1970s (Samuel & Conceição, 2022). Transactional distance theory emerged from efforts to define distance education (Moore, 1997). Moore proposed three dimensions of distance learning in the transactional distance theory: instructional dialogue, course structure, and learner autonomy. Moore described the dimensions as the following:

1. Instructional dialogue: Moore defined instructional dialogue as a positive interaction between parties. However, Moore acknowledged the role that educational philosophies, the environment, personalities, and the design of the course have in the development of instructional dialogue. Moore recognized that the most important factor is the medium used for communication while arguing that with the rapid development of distance education more focus should be placed on the design of courses, the training of course designers, and the different learning styles.
2. Course structure: Moore defined course structure as the design/implementation of learning objectives, teaching approaches, and assessment types. It represents, according to Moore, the level of rigidity or flexibility in a course. Moore acknowledged that over structuring or a lack of structure in a course both impact opportunities for dialogue and learner autonomy.
3. Learner autonomy: Moore defines learner autonomy as the degree to which a learner is capable of engaging with the instructional content without the need for guidance or supervision from the educator. Previous research supports the hypothesis that autonomous learners are more successful in programs with lower levels of dialogue and structure, however Moore stressed that additional research is required to add validity to this claim.

Samuel & Conceição (2022) discussed the theories informing online course design and argued that consequent research in the field supports the notion that these dimensions laid the foundation for future research into different aspects and elements of online course design and instructional design frameworks.

Garrison et al. (2000) developed a framework to determine what constitutes a valuable learning experience through the use of discussions in the online environment, they named it the community of inquiry (CoI) model; the model is shown in Figure 2.2. The CoI model is composed of cognitive presence, social presence, and teaching presence in computer mediated communications and computer conferences used for educational purposes (Garrison et al., 2000). Şen-Akbulut et al. (2022) conducted a study to validate a sub-dimension of the CoI framework where they established cognitive presence as the learner’s knowledge construction in online communities through cognitive engagement, social presence as the learners’ participation in the online learning community, and teaching presence as the design and support of the learners’ social and cognitive presence which is conducive to learning.

Figure 2.2

Community of Inquiry Framework

Community of Inquiry Framework. Garrison et al. (2000)

The community of inquiry framework addresses all three dimensions of the transactional distance theory (instructional dialogue, course structure, and learner autonomy) and accounts for the psychosocial components of learning (Samuel & Conceição, 2022). In an effort to operationalize the community of inquiry framework, Arbaugh et al. (2008) carried out an empirical study and developed a quantitative instrument that evaluates the relationships between the community of inquiry framework and online course variables. Their study validated the 34-item community of inquiry survey as a tool that measures teaching, social, and cognitive presence; however, they acknowledged the fact that it did not account for emerging technologies and their use in role in education.

Şen-Akbulut et al. (2022) observed that the community of inquiry framework has been validated through numerous studies, yet research shows that it does not account for how technology is used. This was established through early studies of online learning effectiveness conducted by Swan (2003) who analyzed the community of inquiry framework and determined that more extensive research was needed into the use of more complex online learning environments. Attempting to expand the community of inquiry framework, Şen-Akbulut et al. proposed the addition of technology elements under each dimension to account for its use and its impact in the overall learner’s experience. Through a study conducted during the 2020-2021 fall term, they developed an extended community of inquiry survey with sub-dimensions and items that account for the technological components of online education within the three foundational dimensions of the community of inquiry framework. They collected data from 653 undergraduate and graduate students at two public, English-speaking universities in Turkey while instruction was taking place fully online as a means of validating the survey. Their study validated the expanded community of inquiry survey as an instrument that can help educators and instructional designers select the best tools for the online learning environment, however they acknowledged limitations in their study’s sample as being limited and proposed that the survey would benefit from further evaluation.

Although the community of inquiry framework is widely accepted and continues to be used (Şen-Akbulut et al., 2022), Anderson (2011) highlights a significant limitation in the model, stating that “the community, however, binds learners in time, and thus forces regular sessions – or at least group-paced learning. Community models are also generally more expensive simply because they cannot scale up to serve larger numbers of students” (p. 61). According to Anderson, online learning theories should allow for independent learning and instead of dictating a format that is constrained by time and location as is required with the collaborative activities under the community of inquiry framework.

Siemens (2004) contended that the most prominent learning theories and frameworks referred to during the development of learning environments failed to account for technology and its impact in learning. Recognizing the importance and implications of technology on learning, he introduced connectivism as an online learning theory that incorporated networked learning. He founded this on the belief that the foundations of knowledge and information flow are rapidly increasing and changing. Siemens proposed eight principles of connectivism, stressing the importance of having access to new knowledge about the digital era. Siemens’s eight principles are illustrated in Figure 2.3. Ultimately, Siemens proposed that connectivism not only impacts learning, but it has implications for management and leadership, the news and media, knowledge management, and instructional design.

Figure 2.3

Siemen’s Eight Principles of Connectivism

Siemen’s Eight Principles of Connectivism. Siemens (2004)

Through further research on existing theories and frameworks of online education, Picciano (2017) acknowledged the fact that “Connectivism is particularly appropriate for courses with very high enrollments and where the learning goal or objective is to develop and create knowledge rather than to disseminate it” (p. 175).

Anderson (2011) argued that the best affordance of the World Wide Web for education was not its increase in the ease of access to content but its interactions and communications capability. Consequently, he sought to define a comprehensive theory for online education, building upon the tenets of the community of inquiry framework and connectivism, and ultimately developed a model of online learning for researchers to expand upon. The model emerged after Anderson recognized the increasing affordances of the internet on learning and suggested that it was too soon to define a theory for online learning. The model is illustrated in Figure 2.4. Anderson determined what has been established by other theorists, that researchers and practitioners often consider online education as a subset of learning and as a subset of distance education. Thus, his model attempts to capture all forms of interaction which he deemed as foundational to an online learning theory. These are interactions between students, teachers, the learning content, and the online educational environment.

Figure 2.4

Anderson’s Model of Online Learning

Anderson's Model of Online Learning. Anderson (2011)

Based on his model, Anderson proposed that meaningful learning can be achieved if the course designer creates a space that is either learner, content, community, or assessment centered, or ideally all at once. Anderson claimed that an online learning theory could encompass existing theories and frameworks except for those that account for in-person interactions in the traditional classroom. However, Picciano (2017) contends that this proposition, excluding face-to-face interactions and blended/hybrid models, complicates the development of a general theory for online education and “is problematic for those who see online education as a subset of education in general” (p. 179). He also argued against Anderson’s claim that self-study and independent work are incompatible with community-based frameworks.

Expanding on computer-mediated communication (CMC) and Siemen’s (2004) networked learning, Harasim (2017) proposed the theory of collaborativism, previously known as online collaborative learning theory (OCL), which Harasim developed in 2012. Harasim argued in favor of collaboration and peer-to-peer knowledge building in online learning environments using the affordances of the internet. The basis of collaborativism, which derived from social constructivism, is that collaboration leads to knowledge construction through three phases: 1) idea generating, 2) idea organizing, and 3) intellectual convergence (Picciano, 2017). Picciano proposed that unlike previous theories, collaborativism views the instructor as a moderator or facilitator, placing the responsibility of knowledge building and sharing on the students and their group interactions. Furthermore, Picciano argued that due to the importance of the instructor’s role, collaborativism, unlike connectivism, is not suited for high-enrollment courses but rather smaller online learning environments. Through a review of the literature, Picciano concluded that the singularities of the theories regarding online learning environments become important when trying to identify commonalities within online learning theories.

Consequently, in an attempt to build a common integrated theory for online learning, Picciano (2017) proposed an integrative model for online education and developed the Multimodal Model for Online Education. Picciano adapted Bosch’s Blending with Pedagogical Purpose Model, developed in 2016 and driven by pedagogical approaches (See Figure 2.5), while drawing parallels between the community of inquiry framework, connectivism, collaborativism, and Anderson’s Online Learning Model to ultimately develop an integrated model centered around the learning community.

Figure 2.5

Bosch’s Blending with Pedagogical Purpose Model

Bosch’s Blending with Pedagogical Purpose Model

Picciano recognized the importance of community, self-paced activities, and other components of existing online and general learning theories and questioned whether Bosch’s model could be expanded or modified through the integration of other learning theories, thus resulting in a general model for online education. Through a review of the literature on online learning theories, he proposed the Multimodal Model for Online Education. The model is shown in Figure 2.6. The purpose of Picciano’s model is to integrate existing learning theories and online learning frameworks.

Figure 2.6

Picciano’s Multimodal Model for Online Education

Picciano’s Multimodal Model for Online Education. Picciano (2017)

Picciano (2017) placed the learning community at the center of his model and incorporated elements that Anderson (2011) had considered incompatible, such as self-study and the community. Picciano provided examples of use for the model and made the claim that the model can be used throughout different modes of online learning environments. Picciano also suggested that behaviorists, cognitivists, and connectivists could adapt the model to meet their needs. However, Picciano acknowledged the limitations and suggested that more research is necessary on the implementation of his model to assess how it meets the needs and demands of various learning theories.

After researching online learning experiences, Conceição (2021) concluded that, as a field, online course design is seeing a shift in focus from teaching strategies and instructional content to a more learner-centered approach. Likewise, Ralston-Berg and Braatz (2021) conducted an analysis of online course design and argued that online course design benefits from providing a holistic experience in online programs for the students and focusing on learner-centered approaches. Conceição proposes the use of learner-centered principles, focused on learner characteristics, as a framework to develop online learning experiences that are meaningful for the adult learner. Through a review of the literature, Conceição and Howles (2021) argued that previous research has supported the belief that learner characteristics have an impact on the cognitive, behavioral, emotional, and social dimensions of learning. The learner characteristics identified by Conceição are prior knowledge, motivation, self-regulation, self-directedness, self-efficacy, and identity. Figure 2.7 shows Conceição’s proposed model for online facilitative strategies with learner-centered principles at the core and professional development as an underlying component for instructors and course designers working with online adult learners.

Figure 2.7

Online Facilitative Strategies

Online Facilitative Strategies Conceição (2021)

The literature reviewed has shown that between the years 2000 and 2022, the research focused on instructional design in higher education has been on the rise (Pollard & Kumar, 2022). Stephens et al. (2022) traced the origins of instructional design to the 1940s during World War II as a collaboration between psychologists and educators to develop military training programs. Bennett and Albrecht (2021) analyzed the role of the instructional designer and found that the demand for instructional designers has increased with a higher demand for online education. Smith and Ragan (2005) define instruction as “the intentional facilitation of learning toward identified learning goals” (p. 4) and instructional design as “the systematic and reflective process of translating principles of learning and instruction into plans for instructional materials, activities, information resources, and evaluation” (p. 4). Tracey and Boling (2013) defined design as a problem solving systemic process based on theory and represented by models. Tracey et al. (2023) later defined instructional design as “a process, where designers follow step by step procedures to create instruction to solve a performance problem” (p. 2054). Through a systematic review of the literature in instructional design models and frameworks, Abuhassna and Alnawaiha (2023) established that instructional design uses learning theory and follows a systematic approach towards facilitating pedagogical and technological solutions. They argued in favor of instructional design as a discipline that bridges the gap between instructors, students, and technology. Furthermore, Bennett and Albrecht (2021) made the claim that instructional designers are critical to higher education institutions.

The literature reviewed explored learner engagement in higher education through the dimensions of behavioral, cognitive, emotional, and social engagement. Fredricks et al. (2004) conducted a review of the research on school engagement and argued that the dimensions of engagement are interconnected, as is often the research within them. Redmond et al. (2018) evaluated online engagement in higher education and proposed a fifth dimension, collaborative engagement, to be used when analyzing higher education engagement in online environments. They proposed the online engagement framework (see Figure 2.9) comprising the five dimensions they argue are key for successful engagement in online learning and teaching environments. Their framework emerged from a social constructionist perspective.

Figure 2.9

Online Engagement Framework

Redmond et al. propose that the dimensions of engagement are interrelated and describe the online engagement framework as a tool for designers to evaluate online student engagement. Li and Xue (2023) further compartmentalized learner engagement in higher education by categorizing the dimensions of engagement into campus engagement and class engagement. They argued that campus engagement focuses on the social components of engagement while class engagement focuses on the behavioral, cognitive, and emotional components of engagement. This literature review explores each dimension of engagement to gain a better understanding of the underlying theories that are at play in the study of learner engagement in higher education.

Zhoc et al. (2019) argue that the literature on engagement in higher education is primarily approached from a behavioral perspective. Similarly, Li and Xue (2023) observed that learner engagement in higher education has been linked to learning outcomes and observable behaviors. However, It wasn’t until 1999, as a result of the publication of A Nation at Risk in 1983, that a tool for measuring engagement emerged–the National Survey of Student Engagement (Axelson & Flick, 2011). The National Survey of Student Engagement measures engagement through a behavioral approach, classifying student behavior as a learned response (Spiker, 2021). Spiker (2021) criticized the National Survey of Student Engagement for its behavioral approach to engagement and lack of consideration of the emotional dimension of engagement. Zhoc et al. (2019) identified the behavioral approach of the National Survey of Student Engagement as measuring the effects of observable variables of engagement in higher education. As a result, other tools to measure engagement in higher education emerged such as the Higher Education Student Engagement Scale (HESES) (Zhoc et al., 2019).

In a study to develop and validate the Higher Education Student Engagement Scale, Zhoc et al. (2019) determined that students’ sense of belonging, which is attributed to emotional engagement, directly impacts social engagement; thus, the Higher Education Student Engagement Scale takes a psychological approach towards engagement in higher education. However, in an effort to develop a comprehensive framework to evaluate engagement in higher education in an online setting, Redmond et al. (2018) propose belonging in a learning community as an attribute of the social dimension of engagement. They describe the learning community as the development of relationships between student-student and student-instructor. Similarly, studying learner engagement in an undergraduate course, Chatterjee (2022) proposed the use of the Communities of Inquiry (CoI) framework to approach learner engagement in formal and informal settings. The Communities of Inquiry framework is composed of three elements: cognitive presence, social presence, and teaching presence Chatterjee (2022).

Furthermore, Zhoc et al. (2019) proposed five dimensions of student engagement in higher education including academic, cognitive, social with peers, social with teachers, and affective. Heilporn et al. (2022) argue that these proposed dimensions of student engagement in higher education align with the dimensions of engagement. According to Heilporn et al. the academic dimension is behavioral engagement, the affective dimension is emotional engagement, and the social with peers’ dimensions incorporates elements of the collaborative dimension of engagement. Redmond et al. (2018) argue that an increasing number of online learners are non-traditional and require different forms of engagement in the online learning environment. They proposed the online engagement framework as a tool for instructional designers to follow when developing online courses in higher education.

Through an analysis of the characteristics of instructional design models, Branch and Merrill (2012) observed that most instructional design models produced after the 1970s contain some element of ADDIE (Figure 2.11). ADDIE follows a waterfall methodology (Wolverton & Hollier, 2022). The steps of ADDIE include analysis, design, development, implementation, and evaluation (Alsaleh, 2020). Each step is described as follows:

• Analysis: The instructional designer identifies the gap, problem, or need, its cause, and determines possible solutions.
• Design: The instructional designer establishes the educational goals, determines a sequence for learning, selects the educational strategies and instructional technology, and defines the assessment tools.
• Development: The instructional designer produces prototypes and iterations of the final product.
• Implementation: The instructional designer adds the product to the live environment and gathers data on the product.
• Evaluation: The instructional designer evaluates the efficiency of the product, using data collected through the implementation phase. If necessary, revisions are made to the product.

Figure 2.11

ADDIE Instructional Design Model

Alsaleh (2020) conducted an action research study with 77 teachers that completed an ADDIE training program where ADDIE was identified as the standard model for instructional design. ADDIE is based on a systematic product development concept and is a highly effective tool in instructional design (Branch & Merrill, 2012). They identified its iterative nature as one of its biggest strengths. Yet, Allen and Merril (2018) argue that ADDIE loses sight of the learner experience and criticize it for being a slow process. Jung et al. (2019) found criticism of ADDIE in the literature reviewed on instructional design due to its systematic, constraining, linear, and time-consuming nature. This was supported through a study by Petherbridge et al. (2022) which interviewed 33 instructional designers to explore the impacts of COVID-19 on their practice and found evidence that some instructional designers stray from the processes of models such as ADDIE when they are working under time constraints. However, results from a multimethod study by Klein and Kelly (2018) which analyzed 393 job announcements pertinent to instructional design and interviewed 20 instructional design project managers suggests that ADDIE continues to be a favored model in the instructional design field. Nonetheless, they voice concern with the possibility of instructional designers being stuck in the status quo with regards to ADDIE. An agile alternative to ADDIE that emerged in the field is the Successive Approximation Model (SAM) (Molenda, 2022).

The Successive Approximation Model (see Figure 2.12), was developed by Allen and Sites in 2012 as an agile alternative to the ADDIE model (Wolverton & Hollier, 2022). Allen and Sites (2012) proposed that the iterative nature of the Successive Approximation Model allows for the design and development of engaging learning products in a flexible manner. They argued that models like ADDIE do not produce consistent quality products and struggle to manage constraints, however the Successive Approximation Model addresses those two concerns through its short work cycles and iterative design. The model is composed of three key phases – preparation, iterative design, and iterative development – and subphases (Allen & Sites, 2012). Jung at al. (2019) conducted a mixed methods study exploring the use of the Successive Approximation Model and their research indicates that it is effective in allowing rapid agile revisions and accommodating the evolving needs of the learner.

Figure 2.12

Successive Approximation Model

In an interview, Allen argued in favor of the Successive Approximation Model because he considers it to be a more natural approach to developing learning experiences and it promotes creativity (Brustin, 2013). Allen and Merrill (2017) further argue that the use of the Successive Approximation Model is more cost-effective to implement because its preparation phase makes it time-efficient. Through a case study which interviewed three instructional designers and one director of distance learning and collected comments from students in the online courses, Wolverton and Hollier, (2022) studied the implementation of the Successive Approximation Model and are in agreement that the model’s continuous feedback and evaluation cycle make it a better alternative when there are time constraints. Although the Successive Approximation Model is a novel alternative to ADDIE, Jung et al. (2019) identified a gap in the amount of empirical research focused on the implementation of the model in real-life scenarios.

A model that emerged as a means to provide guided and meaningful learning opportunities is backwards design (Figure 2.13) developed by Wiggins and McTighe in 1998 (Wiggins & McTighe, 2005). Wiggins and McTighe (2005) questioned the traditional approach of lesson planning where the lesson topic guides the design of the instructional activities. They proposed the backwards design model as an education-specific solution that identifies the expected outcomes and assessment methods at the beginning of the design process and works backwards in the design of the lessons to ensure that the goals are met and students are equipped to succeed in the assessments. Their proposed model has three stages: 1) identify desired results, 2) determine acceptable evidence, 3) plan learning experiences and instructions.

Figure 2.13

Backwards Design Model

Emory (2014) explored the use of backwards design as a way of strengthening the development of curriculum models in nursing programs and claimed that utilizing backwards design could lead to authentic learning and it could better prepare students for licensing examinations in the field. Similarly, through a study that explored the effects of backwards design on students’ intrinsic motivation, Alenezi (2015) determined that when utilizing backwards design students can perceive the learning content as relevant and valuable, thus engaging with it meaningfully in the course. Mills and Williams (2019) explored the use of backwards design in 37 sections of a first-year writing course where they gathered empirical evidence from 178 students which suggests that instructors and students deeply engage with the learning content when lessons are designed using the backwards design model. However, McCreary (2022) takes issue with the instrumentalization of the educational process that happens when utilizing backwards design. McCreary argues that valuable learning opportunities might get overlooked because they do not align with the learning outcomes; they suggest that instructors design spaces where students can make connections between what they are learning and the world and that students be given the opportunity to reflect on what they already know and what they bring to the classroom.

The literature reviewed also identified a framework focused on the user experience (usability) of instructional design products. Through a study that explored the link between student usability and instructional design best practices, Ngampornchai et al. (2021) identified that the quality of online courses is traditionally measured through instructional or pedagogical principles. They described instructional design principles as being concerned with learning theories and strategies. However, they argue that usability, that is the users’ experience navigating the course and the ease with which they can complete tasks in that environment, is essential for high quality instructional design because poor usability can decrease effective learning. Koohang and du Plessis (2004) conducted research on usability in e-learning and instructional design and they found evidence that usability has a positive effect on student learning in the online environment and, when ignored, can prevent learning. They argue that usability cannot be excluded from instructional design frameworks and proposed a usability framework for instructional design (see Figure 2.14) focused on the properties of usability that they identified as essential for instructional design. The properties fall under two paradigms: looks great and works well. Under looks great is the presentation property and under works well are the properties of navigation, communicative enablement, technical functionality, and learner support.

Figure 2.14

E-Learning Usability Properties Framework

Further researchers sought to develop evaluations for usability and the instructional design of a course. Landa argued that establishing flow through a visual hierarchy when designing online courses is an important component as it gives students a sense of direction (Landa, 2018). In an attempt to develop a usability evaluation method for e-learning applications, Zaharias and Poylymenakou (2009) attributed poor instructional design features and usability of online training and corporate courses to the high attrition rates of online learning programs. They proposed an E-Learning usability Evaluation Questionnaire based on a usability and instructional design framework that they developed. Their proposed framework has five usability attributes and seven instructional design attributes. The usability attributes include navigation, learnability, accessibility, consistency, and visual design. The instructional design attributes include interactivity/engagement, content and resources, media use, learning strategies design, feedback, instructional assessment, and learner guidance and support. Phillips et al. (2016) implemented Zaharias and Poylymenakou’s framework in an online course to evaluate whether usability and instructional design were a factor on student stress and motivation. They had previously identified hindrance stress, which interferes with the learning process, and challenge stress, which nurtures learning and promotes cognitive processes, as two factors expected to be impacted by instructional design and usability. Phillips et al. collected data from 20 students and determined Zaharias and Poylymenakou’s framework for usability evaluation was effective in identifying hindrance stress of students in an online course. In a subsequent study, Sandoval et al. (2016) adapted the E-Learning usability Evaluation Questionnaire for higher education by adding two criteria: assessment and instructor presence. They tested the adapted questionnaire with 520 students enrolled in online courses and argued that the use and results from the questionnaire can help improve online course design and user experience. Ngampornchai et al. (2021) studied online course usability with 12 college students enrolled in one of three different versions of an online course. Their data suggested that instructional design principles and usability principles be integrated for an optimal course design. They evaluated the students’ perception of course components recommended by instructional frameworks. The components are: including the homepage, learning objectives, having an orientation module (sometimes called “Module 0” or “Getting Started”), and having an instructor self-introduction (e.g., written introduction, greeting audio, video introduction). They also explored student navigation of the online course and found evidence that students favor courses that follow instructional design principles. Furthermore, they argue that the study reinforced the importance of inclusive course design.