Over the past three decades, instructional models such as the flipped classroom, inverted classroom, flipped learning, or inverted learning have gained considerable traction, drawing the interest of both educational practitioners and researchers (Lee, 2023). Although these terms share similar definitions, they have gradually given way to the term ‘flipped learning’, hereafter abbreviated as FL in this paper. In 2012, FL emerged as an experimental learning approach in a high school in the United States (Bergmann & Sams, 2012). Presently, FL enjoys widespread recognition across diverse educational settings (Lee & Choi, 2019). This paper aims to explore the historical path of FL, the underlying theories guiding its implementation, and an examination of its potential benefits and challenges. We will argue that FL, by utilising modern technology and innovative teaching strategies can significantly enhance student engagement and academic outcomes while introducing substantial challenges that educators must address. Lastly, we will explore the future prospects for FL, considering various opportunities and threats posed by advancements in artificial intelligence technologies and their potential impact on FL within the classroom.

Figure 1. A History of Flipped Learning. Video created with Doodly (2024), Elevenlabs (2024) and Ableton (2024).

The complex evolution of FL has given rise to multiple definitions. Perspectives range from the utilisation of traditional methods of in-class knowledge transmission to more contemporary forms of blended learning. Bergmann & Sams (2012) originally proposed a more traditional approach to FL, defining it as a model where classwork is conducted at home, and homework is completed in-class. Other definitions have incorporated the inclusion of group and collaborative work within the classroom (Bishop & Verleger, 2013; Lo & Hew, 2017; Brewer & Movahedazarhouligh, 2018; Bond, 2020). However, as blended learning offers a blend of synchronous face-to-face learning and asynchronous online experiences (Garrison & Kanuka, 2004; Garrison & Vaughan, 2008), FL may also involve content learning online before class, followed by active learning during face-to-face classroom activities (Chen et al., 2018). The traditional FL definition (Bergmann & Sams, 2012) also allows for the use of various media types, including video (Lee et al., 2016; Lo & Hew, 2017), reading assignments and PowerPoint presentations (Lai & Hwang, 2016; Giannakos et al., 2018; Lee & Choi, 2019). These pre-class activities can be delivered through diverse online platforms such as learning management systems, Microsoft Teams and YouTube (Caligaris et al, 2016; Wang, 2017; Yilmaz, 2017). In the wake of the COVID-19 pandemic, newer FL approaches have emerged including bichronous and hyflex learning, which use different combinations of synchronous and asynchronous activities (Parra & Abdelmalak, 2016, Viriya, 2022). Synthesising these perspectives, we will therefore define FL as a teaching strategy that uses various forms of media or solutions to facilitate pre-class online knowledge acquisition and in-class collaborative knowledge application.

Figure 2. Introduction to Flipped Learning (Bergmann, 2023).

Bergmann & Sams (2012) and Eppard & Rochdi (2017) have discussed how FL is based on Bloom’s mastery theory, allowing students to learn at their own pace. Each discrete knowledge objective should be mastered to enable the success of subsequent sections. Bergmann & Sams (2012) also explained that technology has now enabled the mastery theory, where historically (in the 70s and 80s) the practicalities of independent or group learning were difficult to manage.

Talbert (2012) and Eppard & Rochdi (2017) described the transmission and assimilation of learning as an important part of FL. The transmission of information is acquired independently out of class, while assimilation occurs during class, which requires greater critical reasoning. Eppard & Rochdi (2017) associated the transmission of information with behaviourism. Ertmer & Newby (2013) explained that behaviourism focuses on the consequences of learning performances and responses to enable the repetition of behaviour. This method facilitates learning that involves recalling facts, generalisations, applying explanations, and performing specified procedures automatically.

The success of FL is further supported by constructivist learning theories, particularly Piaget’s (1964) cognitive constructivism and Vygotsky’s (1978) social constructivism. Ertmer & Newby (2013) described constructivism as a theory that enables learning by creating meaning from experience. Constructivist teachers use instructional methods and strategies to help learners explore and think more deeply about subjects. Vygotsky’s (1978) social constructivism suggests that learners construct knowledge through social interaction, interpretation, and understanding. This theory emphasises that knowledge creation is inherently linked to the social context in which it occurs, framing learning as an active process of knowledge construction.

Vygotsky considered learning a process where students are assisted by more competent individuals, optimising learning through collaboration within the learner’s zone of proximal development. He defines this zone as the difference between the actual developmental level achieved by independent problem-solving and the potential level achieved through guidance or collaboration. Within the FL model, teachers create opportunities for problem-solving, peer learning, and active learning (Abeysekera & Dawson, 2015), thus enabling access to social constructivism. Adams (2006) points out that constructivism focuses on learning rather than performance, encouraging learners to be co-constructors of meaning and knowledge.

Piaget’s (1964) cognitive constructivism theory aligns with Vygotsky’s, maintaining that higher levels of learning require peer interaction. The theory of cognitive development underpins new knowledge acquisition through experiences, enabling learners to create mental models that can be refined and made more sophisticated through assimilation (Eppard & Rochdi, 2017). Piaget (1964) emphasised the active role of learners in constructing their understanding of concepts through firsthand experiences and interactions with their environment. In FL, students engage with pre-class materials independently, constructing knowledge before participating in collaborative activities during face-to-face or synchronous sessions. The self-directed nature of FL encourages students to explore, reflect, and make meaning of content at their own pace, aligning with Piaget’s (1964) emphasis on active learning and cognitive development. By grappling with concepts independently before engaging in discussions and problem-solving activities, students are more likely to internalise knowledge and develop a deeper understanding, consistent with Piaget’s principles of assimilation and accommodation.

Eppard & Rochdi (2017) proposed that FL methodology is successful due to the juxtaposition and dynamic interaction of these different learning theories and models. Figure 3 (below), adapted from Eppard & Rochdi (2017), visualises this interaction based on Anderson et al.'s (2001) updated model of Bloom's Taxonomy. The diagram illustrates the overlap between learning theories as part of the FL process. The theory of behaviourism links with the transmission of information and the remembering and understanding foundation of knowledge. This foundation enables learners to work with peers to analyse and apply their knowledge in a social constructivist way, assimilating knowledge. Finally, these interactions make possible the creative and evaluating sections of Bloom’s Mastery Theory using cognitive constructivism.

Figure 3. Visualisation of Learning Theories. Adapted from Eppard and Rochdi (2017).

Figure 3 shows that the majority of most time and effort is directed towards the initial behaviourist sections of remembering and understanding. A different model can be presented (Figure 4) if used with Bergmann & Sams' (2014) version of Bloom's Taxonomy and FL. This reveals how the different theories combine and influence each other. In this model, the primary focus is on the analysis and application of knowledge within the classroom, aligning with the principles of social constructivism. Conversely, the behaviourist components of knowledge acquisition and information transmission are afforded less emphasis.

Figure 4. Learning Theories adaptation from Eppard & Rochdi (2017) combined with Bergmann & Sams (2014) version of Bloom's Taxonomy.

Since 2012, numerous studies have proclaimed the potential benefits of FL (Bergman & Sams, 2012; Enfield, 2013; Farah, 2014; Eppard & Rochdi, 2017). Akcayir & Akçayır (2018) underscored the efficient utilisation of in-class time, which facilitates more learner-centred activities such as group discussions, interactive activities, and feedback. Learners often construct knowledge both inside and outside the classroom through activities and assignments that promote critical thinking and problem-solving skills (Brewer & Movahedazarhouligh, 2018). As such, Rahmen et al. (2020) suggested that a paradigm shift in learning and teaching styles is needed. However, Eppard & Rochdi (2017) stressed that not all areas of success with FL are yet understood. Obstacles such as learners' unfamiliarity and reluctance towards FL can impede its implementation, potentially causing anxiety and resistance to change (Akcayir & Akçayır, 2018; Brewer & Movahedazarhouligh, 2018). Additionally, educators may resist the paradigm shift from being a 'sage on the stage' to a 'guide on the side' (Baker, 2000).

Nevertheless, much of the research concerning the effectiveness of FL indicates improved academic and learning outcomes, predominantly assessed through course grades or test scores (Love et al., 2013; Mattis, 2014; Sowa & Thorson, 2015). For instance, Day (2018) conducted a comparative study in Boston, examining two groups of students: one employing traditional teaching methods and the other adopting a flipped classroom. The results demonstrated significant disparities, with the flipped classroom group attaining higher grades than their peers in the traditional setting. Similarly, by using a combination of pre-recorded lectures, online quizzes and in-class group activities, Awidi & Paynter (2019) affirmed that the FL model had shown transformative potential in boosting student engagement and enhancing learning outcomes. However, a three-year study comparing the traditional model to a flipped format, found that higher learning outcomes require more effort and attention to detail than the traditional teaching format (Taha et al., 2016).

Another outcome frequently explored in FL research is student engagement or motivation. It has been reported that students experience high levels of motivation and engagement in their learning (Awidi & Paynter, 2019; Gündüz & Akkoyunlu, 2019; Jian, 2019). A review by Akcayir & Akçayır (2018) highlighted that the flipped approach boosted student motivation by up to 18% and active engagement by up to 14%. Schmidt & Ralph (2016) also found that FL increased student engagement by 80%. However, some findings reveal challenges with student motivation and self-regulation, particularly during pre-class learning. Heitz et al. (2015) reported that a significant number of students often fail to complete assigned pre-class tasks. Given that these activities are integral to FL, their completion directly affects FL's effectiveness. Lee (2023) suggested that distractions during online content viewing, feelings of helplessness when facing difficulties, or the need for clarification can lead to disengagement. Therefore, it is crucial for educators to reinforce the importance of pre-class learning as a foundational element of FL. Furthermore, Yilmaz (2017) noted a decrease in motivation with FL, attributing this to students' technological capabilities to access the pre-classroom content. To enhance student self-regulation, strategies such as reviewing students’ written notes, administering online quizzes, or providing structured study schedules could be effective (Cheng et al., 2019). Additionally, Sergis et al. (2018) advocated for the use of self-determination theory, a psychological framework for understanding motivation developed by Ryan & Deci (2000). By meeting the three basic needs of autonomy, competence, and relatedness, this can significantly improve cognitive learning outcomes and increase student satisfaction with the FL process.

As Bergmann & Sams (2012) observed, the cornerstone of an effective flipped classroom is technology. The use of technology in FL provides an opportunity to integrate digital tools into the classroom and mediate non-digital processes and practices (Eppard & Rochdi, 2017). The benefits include capabilities to skip forward, pause, and rewind videos (Lipomi, 2020), and to repeat or learn additional content at the student's own pace (Brewer & Movahedazarhouligh, 2018; Birgili et al., 2021), thus offering a more flexible approach to individual learning. According to Selwyn (2016), digital technology can empower educational change and enable the transformation of educational practices.

However, students and educators often confront technical challenges related to infrastructure, access to devices, and proficiency in technology skills (Lee, 2023). Access to online pre-recorded videos and content can pose significant hurdles for students lacking electronic devices (Gilboy et al., 2015). Furthermore, some may face difficulties due to insufficient experience in navigating technology and online learning systems. This could promote inequalities, hinder students' progress and increase the digital divide (Selwyn, 2016). For FL to be effective for all learners, issues related to the digital divide need to be addressed. Selwyn (2016) summed this up: "In striving to make the best use of technology in education, surely we need to ensure that all forms of digital education are pursued primarily in the general interests of the public rather than the narrow interests of the well-resourced and privileged few?" (pp. 159).

Similarly, teachers may have to deal with internet connectivity issues and struggle to upload video lectures (Gilboy et al., 2015). Additionally, these videos may be of poor-quality owing to inadequate ICT skills (Lo & Hew, 2017). For successful online content delivery, teachers require the appropriate technological tools to record, edit, and publish video lectures, a process that demands meticulous planning, time, and effort (Asad et al., 2022). As such, Bergman (2014) identified technology as a significant challenge in adopting the flipped classroom model, underscoring the necessity to carefully align the choice and application of technological tools with the specific classroom context. Conole (2015) proposed the 7Cs Learning Design Framework, designed to aid educators in making pedagogically sound decisions that appropriately utilise digital technologies (figure 5).

Figure 5 – The 7Cs Learning Design Framework (Conole, 2015). Created with Adobe After Effects (2024), Elevenlabs (2024), Ableton (2024) and Camtasia (2024).

Conole (2015) noted that educators often struggle with effective planning due to insufficient digital literacy, lack of support, and limited time to experiment with technologies. In this case, lecture materials could incorporate Open Educational Resources (OER) such as variety of existing video learning content from MOOCs or platforms like TED (2024) (figure 6). These resources, alongside the use of the 7C’s Learning Framework, can provide vital support for addressing significant technological challenges that educators encounter in designing and implementing the FL model.

The use of technology in education has also introduced terms such as blended, bichronous and hyflex learning. A search on Google Scholar for FL in conjunction with blended learning provides almost 98,000 results, indicating significant interest and a link between FL and blended learning.

Figure 7. Blended, Bichronous and Hyflex Learning in a Flipped Learning Context. Presented by Samantha Watson (2024). Created with Ableton (2024) and Camtasia (2024).

The emergence of Generative Artificial Intelligence (GAI), capable of generating new content from training data, has sparked significant debate regarding its potential impact on future FL practices (Gilson et al., 2023; Su & Yang, 2023; Tlili et al., 2023; Farrokhnia et al., 2024). These models are engineered using a blend of deep learning techniques and extensive pretraining on large datasets, which equips them to decipher language patterns and relationships. This ability enables them to produce contextually relevant responses (Lo, 2023). A prominent example is Chat GTP, a GAI chatbot which can assist, mentor, or support students in learning course materials (Wollny et al., 2021). While some studies praise chatbots as effective tools for facilitating adaptive learning and access to information (Gilson et al., 2023; Su & Yang, 2023), other research underscores the potential risks these technologies pose to educational practices (Tlili et al., 2023; Farrokhnia et al., 2024).

Diwanji et al. (2018) proposed that chatbots can uphold the fundamental principles of the FL classroom, such as self-directed learning and learner-centered instruction. Furthermore, Han et al. (2022) suggested that chatbots can be programmed to facilitate self-directed learning and provide study tips, enhancing student autonomy and creating a sense of ownership over their learning journey. Huang et al. (2019) and Chen et al. (2020) noted that immediate feedback, aligned with specific learning objectives, can contribute to improved student performance and learning outcomes. This feedback allows educators to tailor instruction to meet individual learning needs. Additionally, Winkler & Söllner (2018) observed that chatbots have the potential to create learning materials customised to the specific needs of students, further facilitating personalised learning experiences and thereby increasing engagement and motivation.

However, the integration of chatbots within the FL framework remains under-researched, prompting Bahja et al. (2020) to underscore ethical and privacy concerns as a significant challenge for educators. Institutions should be tasked with ensuring the non-retention of personal data, maintaining student privacy, and securing data storage (Hasal et al., 2021). Compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) in the European Union (Regulation GDPR, 2018), is crucial. Additionally, Valério et al. (2020) suggested that chatbots should provide opt-in or opt-out features to protect students who prefer not to participate. Another major issue is the reliability and validity of the information chatbots provide, which depends on the quality of the data they are programmed with, potentially leading to inaccurate or inappropriate feedback (Chuah & Kabilan, 2021; Vanichvasin, 2021). Consequently, future research should focus on methods to enhance the reliability and validity of chatbot feedback, while also addressing potential biases (Kim et al., 2021). Moreover, educators should consider the level of human interaction available to students and create opportunities for meaningful and personalised engagement (Furrer et al., 2014).

As discussed previously, a key outcome frequently explored in FL research is student engagement or motivation, particularly in pre-class learning. Maslow (1981) highlighted that motivation is a critical factor in successful learning. However, the present FL approach often employs learning motivation theory based on reward and punishment mechanisms, termed extrinsic motivation. Rob (2014) noted that this type of motivation leads many students to engage with course materials primarily for the reward of grades. Conversely, Tileston (2010) argued that students perform better when they are intrinsically motivated, where motivation stems first from an interest in the activity itself, enhancing the learning experience (Huang et al., 2023). Recent methods that foster intrinsic motivation include game-based learning (Hwang et al., 2013; Molins-Ruano et al., 2014) and mobile learning (Huang et al., 2016).

Building on Klemm’s (2002) work on conversational theory, Diwanji et al. (2018) posited that GAI could significantly increase intrinsic motivation in FL through written dialogues between teachers and students. Utilising a design science methodology, they developed a chatbot integrated with a dashboard for students and a recommendation system for teachers to facilitate students’ preparation for FL. This chatbot not only initiates but also engages in conversations relevant to course content, thereby enhancing classroom discussions. It is programmable to utilise specific topics and can dynamically form student groups for discussions based on their backgrounds, interests, and motivation levels, creating engaging and diverse interactions (figure 8).

Figure 8: Concept of chatbot supported application for the flipped classroom (Diwanji et al., 2018).

To further mitigate the potential negative impacts of GAI on educational practices, Abdulmalik et al. (2023) proposed a modified flipped learning model (MFL) designed to enhance student learning outcomes and equip them with essential skills such as critical thinking, creativity, and adaptability (figure 9). This model integrates elements of FL but includes tailored modifications to incorporate GAI technology effectively. It shifts the traditional role of the teacher to facilitate student ownership of learning, with students using GAI for pre-class activities and content creation. Under the guidance of their teachers, students refine this content in class, thus becoming co-creators of knowledge and creating a deeper sense of ownership and creativity. The model also encourages collaboration and peer-to-peer learning as students share and discuss their AI-generated content during class sessions, enhancing their understanding of the subject matter. Addressing biases, and ensuring accuracy and reliability are central concerns of the MFL model, which advocates for responsible and ethical usage of GAI in and out of the classroom.

Figure 9. The Modified Flipped Learning Model (MFL) (Abdulmalik et al., 2023).

Figure 10. Flipped Learning Policy Considerations. Presented by Samantha Watson (2024). Created with Adobe After Effects (2024), Ableton (2024) and Camtasia (2024). 

In conclusion, FL has demonstrated significant potential in transforming traditional educational practices by nurturing deeper student engagement, enhancing critical thinking skills, and improving academic outcomes. The theoretical foundations of FL, rooted in Bloom's mastery theory and constructivist learning theories, support its effectiveness in promoting student-centered learning. However, the implementation of FL is not without its challenges. Technological barriers, such as access to devices and digital literacy, can impede its adoption. Additionally, students' unfamiliarity with the FL model and the increased responsibility for pre-class preparation can affect motivation and engagement. Despite these challenges, numerous studies have shown that FL can lead to higher academic performance and greater student satisfaction when effectively implemented.

The integration of generative AI into FL presents new opportunities to further personalise learning and provide real-time feedback, potentially enhancing student motivation and learning outcomes. However, it also raises concerns about data privacy, academic integrity, and the digital divide. Addressing these issues requires careful planning and the development of clear policies to ensure ethical and effective use of AI in education.

Overall, while flipped learning offers promising benefits, its success depends on overcoming the associated challenges and leveraging technological advancements responsibly. By focusing on student engagement, motivation, and the thoughtful integration of AI, educators can harness the full potential of FL to create more dynamic, interactive, and effective learning environments.

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