Assessing the attention levels of students by using a novel attention aware system based on brainwave signals

Rapid progress in information and communication technologies (ICTs) has fueled the popularity of e-learning for educational purposes. However, an e-learning environment is limited in that online instructors cannot monitor immediately whether students remain focus during online autonomous learning. Therefore, this study develops a novel attention aware system (AAS) capable of recognizing students' attention levels accurately based on EEG signals, thus having high potential to be applied in providing timely alert for conveying low-attention level feedback to online instructors in an e-learning environment. To construct AAS, attention responses of students and their corresponding EEG signals are gathered on a continuous performance test (CPT), i.e. An attention assessment test. Next, the AAS is constructed by using training and testing data by the NeuroSky brainwave detector and the support vector machine (SVM), a well-known machine learning model. Additionally, based on the discrete wavelet transform (DWT), the collected EEG signals are decomposed into five primary bands (i.e. Alpha, beta, gamma, theta and delta) as well as each primary band contains five statistical parameters (including approximate entropy, total variation, energy, skewness and standard deviation), thus generating twenty five potential brainwave features associated with students' attention level for constructing the AAS. An attempt based on genetic algorithm (GA) is also made to enhance the prediction performance of the proposed AAS in terms of identifying students' attention levels. According to GA, the seven most influential features are selected from twenty-five considered features, parameters of the proposed AAS are optimized as well. Analytical results indicate that the proposed AAS can accurately recognize individual student's attention state as either a high or low level, and the average accuracy rate reaches as high as 90.39 %. Moreover, the proposed AAS is integrated with a video lecture tagging system to examine whether the proposed AAS can accurately detect students' low-attention periods while learning about electrical safety in the workplace via a video lecture. An experiment is designed to assess the prediction performance of the proposed AAS in terms of identifying the periods of video lecture with high-or low-attention levels during learning processes. Analytical results indicate that the proposed AAS can accurately identify the low-attention periods of video lecture generated by students when engaging in a learning activity with video lecture. Results of this study demonstrate that the proposed AAS is an effective attention aware system, capable of assisting online instructors in evaluating students' attention levels to enhance their online learning performance.

Video lectures are one of the primary learning resources embedded in e-learning environments. Many universities prepare video lectures and share them as open course materials with students. In order to make the learning process more efficient and effective, it is very important to design and personalize those learning resources for learners, taking into their needs and cognitive differences. The literature regarding how cognitively diverse users benefit from which media design is scarce. Therefore, the purpose of this research is to explore the effect of video lectures types (voice over type, picture-in-picture type, and screencast type) and learners’ sustained attention levels (low, and high) on their learning performance in an e-learning environment. The research was designed as a 2 × 3 factorial design. In addition, learners’ eye movements have been recorded during study sessions. Results indicate that main effect of learners’ sustained attention levels and video lecture types on learning performance were significant. Furthermore, it was observed that both for the students with high level of sustained attention and for the students with low level of sustained attention, the use of picture-in-picture types of video lectures led higher learning performance scores. Similarly, the eye tracking analyses also supported this finding.

The pandemic phenomenon has forced the world of education to change the work pattern of services from conventional to online-based education and learning services. This study aimed to describe and analyze the level of teachers’ 21st century proficiency and its effect on teachers’ performance in online learning due to Covic-19. This research was conducted at the Alkautsar Integrated Islamic Education Foundation School, with a population of 90 teachers and a sample of 68 teachers. This is a descriptive quantitative research conducted by a survey design. The main tool for data collection is a questionnaire. Data were analyzed using descriptive statistics by calculating the mean value of teachers' 21st century competence and teachers’ performance in online learning due to Covic-19. Inferential statistical analysis was also carried out to find the magnitude of the relationship and influence between variables. The results showed that the average score of level of teachers’ performance in online learning was 3.56 in the high category. The 21st century proficiency level of teachers with a mean value of 3.72 was also in the high category. It was also found that there is a significant relationship between 21st century proficiency variables and teachers’ performance in online learning of 0.809. The effect of 21st century skills on teachers’ performance in online learning was 65.50%. The conclusion is that the better the 21st century skills of teachers, the better the teacher's performance in online learning due to Covid-19.

ABSTRACTOnline video-based learning has been increasingly used in educational settings. However, students usually do not have enough cognitive capacity and metacognition skills to diagnose and record their attention status during learning tasks by themselves. This study thus presents an attention-based video lecture review mechanism (AVLRM) that can generate video segments for review based on students’ sustained attention status, as determined using brainwave signal detection technology. A quasi-experiment nonequivalent control group design was utilized to divide 55 participants from two classes of an elementary school in New Taipei City, Taiwan, into two groups. One class was randomly assigned to the experimental group, and used video lectures with the AVLRM support for learning. The other class was assigned to the control group, and used video lectures with autonomous review for learning. Analytical results indicate that students in the experimental group exhibited significantly better review effectiveness than did the control group, and this difference was especially marked for students who had a low attention level, were field-dependent, or were female. The findings show that AVLRM based on brainwave signal detection technology can precisely identify video segments that are more useful for effective review than those picked by student themselves. This study contributes to the design of learning tools that aim to support independent learning and effective review in online or video-based learning environments.

Online video-based learning has been increasingly used in educational settings. When viewing video content, students may not always maintain a high degree of attention and may neglect or ignore important content critical for effective learning. This study thus presents an attention-based video lecture review mechanism (AVLRM) that can generate video segments for review based on students' sustained attention status, as determined using brainwave signal detection technology. A quasi-experiment nonequivalent control group design was utilized to divide 55 participants from two classes of an elementary school in New Taipei City, Taiwan, into two groups. One class was randomly assigned to the experimental group, and used video lectures with the AVLRM support for learning. The other class was assigned to the control group, and used video lectures with autonomous review for learning. Analytical results indicate that students in the experimental group exhibited significantly better review effectiveness than did the control group, and this difference was especially marked for students who had a low attention level, were field-dependent, or were female. The findings show that AVLRM based on brainwave signal detection technology can precisely identify video segments that are more useful for effective review than those picked by student themselves.

Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance

Although online courseware often includes multimedia materials, exactly how different video lecture types impact student performance has seldom been studied. Therefore, this study explores how three commonly used video lectures styles affect the sustained attention, emotion, cognitive load, and learning performance of verbalizers and visualizers in an autonomous online learning scenario by using a two-factor experimental design, brainwave detection, emotion-sensing equipment, cognitive load scale, and learning performance test sheet. Analysis results indicate that, while the three video lecture types enhance learning performance, learning performance with lecture capture and picture-in-picture types is superior to that associated with the voice-over type. Verbalizers and visualizers achieve the same learning performance with the three video types. Additionally, sustained attention induced by the voice-over type is markedly higher than that with the picture-in-picture type. Sustained attention of verbalizers is also significantly higher than that of visualizers when learning with the three video lectures. Moreover, the positive and negative emotions induced by the three video lectures do not appear to significantly differ from each other. Also, cognitive load related to the voice-over type is significantly higher than that with by the lecture capture and picture-in-picture types. Furthermore, the cognitive load for visualizers markedly exceeds that of verbalizers who are presented with the voice-over type. Results of this study significantly contribute to efforts to design of video lectures and also provide a valuable reference when selecting video lecture types for online learning.

As video lectures are gaining more popularity, determining their effectiveness and obtaining valuable feedback have become necessary. To measure the learners’ attention state during video lectures, we specified the conceptual “with-me-ness” (WMN) as slide-level WMN (SL-WMN). The content domain on each slide was automatically extracted via an optical character recognition (OCR)-based method, while the eye gazing behaviors were analyzed through a Gaussian mixture modeling (GMM) fixation clustering method. Both domain-specific WMN and behavior-enriched WMN were then computed via OCR- and GMM-OCR-based methods to measure the learners’ attention levels. We conducted an experiment to collect in-lecture eye-tracking data, video recordings, and post-lecture test scores from 50 Grade 8 students. The results demonstrated that both OCR- and GMM-OCR-based SL-WMNs are reliable and compatible automatic measurements of learners’ attention states during video lectures. A survey from participating learners and lecturers also revealed highly favorable feedback for the developed SL-WMNs.

Instructors' speech and gestures are tightly integrated. However, little is known about the neural mechanisms by which different types of gestures affect learning. We conducted two experiments on the effects of gestures in video lectures that included an instructor and slides, with English vocabulary as the topic. In Experiment 1, we manipulated the instructor's gestures (beat gestures vs. pointing gestures vs. depictive gestures) on neural oscillations and learning performance. The electroencephalogram results showed that students had higher alpha power and higher beta power when the instructor used pointing gestures, suggesting lower sensorimotor involvement in processing. Pointing gestures produced lower learning performance than beat gestures and depictive gestures. In Experiment 2, we manipulated the instructor's pointing gestures and the richness of visual materials in video lectures. The results showed that the effectiveness of pointing gestures was moderated by the richness of the visual materials on the slide. Specifically, pointing gestures were more effective for complex versus simple learning materials. The findings suggest that electroencephalogram oscillations and learning performance vary with the type of gesture the instructor uses and the richness of visual materials in video lectures. This study has applied value for designing effective video lectures in many disciplines: (1) When a video lecture includes simple visual materials, a beat gesture and depictive gesture would be better; (2) when a video lecture includes complex visual materials, the instructor can produce pointing gestures to single out the learning content they are talking about. Practitioner notes What is already known about this topic Instructors commonly produce gestures while they teach, and their gestures and speech are tightly integrated. Instructors often use three types of gestures: beat gestures, pointing gestures and depictive gestures. Different types of gestures influence learning via different neural mechanisms. What this paper adds Students' alpha power and beta power had lower amplitudes when observing an instructor's beat gestures, compared to pointing and depictive gestures. Students showed higher learning performance after viewing the video lectures with the instructor's beat and depictive gestures. An instructor's pointing gestures facilitated learning performance primarily in video lectures with complex visual materials (CVM) rather than simple visual material (SVM). Implications for practice and/or policy When a video lecture includes SVM, the instructor is encouraged to produce beat gestures and depictive gestures rather than pointing gestures. When a video lecture includes CVM, the instructor is encouraged to produce pointing gestures to single out the learning content they are talking about.

This study aimed to determine: (1) the correlation between online media literacy and learning performance in term of self-confidence; (2) the correlation between online media literacy and learning performance in termof learning motivation; and (3) the correlation between online media literacy and learning performance in term of students’ semesters. This research was correlational study which was conducted from April to May 2021. The population of this research were active students of the Accounting Education Study Program, Faculty of Teacher Training and Education, Sanata Dharma University, totaling 204 students. The samples in this study amounted to 135 students who were taken by using proportional random sampling technique. The data were analyzed by product moment correlation. The results of this study indicated that: 1) there was a correlation between online media literacy and learning performance in student respondents with high self-confidence (Sig. (2-tailed) = 0,037), while there was no correlation between online media literacy and learning performance in student respondents with low self-confidence. (Sig. (2-tailed) = 0,095); 2) there was a correlation between online media literacy and learning performance in highly motivated student respondents (Sig. (2-tailed) = 0,038), while there was no relationship between online media literacy and learning performance in respondents low- motivated students (Sig.(2-tailed) = 0,488); and 3) there was a correlation between online media literacy and learning performance in high-semester student respondents (Sig. (2-tailed) = 0,014), while there was no correlation between online media literacy and learning performance in the lower semester tiered student respondents (Sig. (2-tailed) = 0,284).Keywords: Online media literacy, learning performance, self-confidence, learning motivation, and students’ semesters

The teaching and learning activities of any undergraduate curriculum will have a specific set of learning outcomes that should be successfully achieved by the students. The balance between the workload of a student and the available time to achieve the learning outcomes plays a major role in achieving these learning outcomes, as well as a good student satisfaction score and excellent final grades for that particular module (Whillier & Lystad, 2013). In a traditional educational experience, a teacher stands in front of the classroom, delivers a lecture to a group of students, who sit in rows, quietly listening to the lecture and taking notes. At the end of the lecture, students are given homework or an assignment to be completed outside of the classroom environment. This characterises the principle of “sage-on-the stage”, and is synonymous with the present day term of teacher-centered learning. This is also referred to as the transmittal model (King, 1993), which assumes that the students are passive note-takers, receivers of the content or accumulators of factoids (Morrison, 2014). Usually, the teacher does not have time to interact with the students individually during the class (Hamdan, McKnight, McKnight & Arfstorm, 2013), thus neglecting those students who do not understand the lecture. The traditional didactic way of teaching is primarily unidirectional and consists of limited interactions between the source of knowledge (teacher) and the passive recipients (students). One of the main challenges faced by lecturers is the overload of academic content that needs to be taught in a relatively short period of time. Equally, the main challenge faced by the students is loss of interest or motivation to learn within the stipulated period of time (Prober & Khan, 2013). The traditional way of teaching, therefore, discourages the students from active learning and critical thinking. There is also increasing pressure from accreditation institutions, which demand “an ability to communicate effectively”, “an ability to identify, formulate and solve problems”, and “an ability to function on multidisciplinary teams” (Bishop & Verleger, 2013). As such, there is a need to transform the current pedagogical strategies, in order to enhance active learning in a more effective way (Al Faris et al., 2013). Synthesis of research on the effectiveness of lectures shows that lectures are not very effective for teaching and developing values or personal development, and may only be effective for the sole goal of transmitting information (Bligh, 2000). Taking these points together, it is important to explore methods that have the potential to maximise the use of classroom time and transform the classroom into a platform for teacher-student interactions and critical thinking (Rui et al., 2017). Numerous factors have cumulatively led to several challenges for traditional teaching in health professional education (HPE), including the availability of digital technologies, digitally-empowered learners, the prolific expansion of courses, the amount of factual knowledge that has accumulated in the courses, prolific growth of health knowledge, advancement in healthcare disciplines, and investment into the scholarship of teaching and learning. To this end, newer delivery systems encompassing active learning in HPE have been developed. Studies have reported that active participation is an effective method to improve learning and understanding (Freeman et al., 2014; McCoy et al., 2015). Thus, to enhance interaction during their learning, there are educational strategies, which promote active learning in traditional lectures by engaging students in doing things and encouraging them to think about what they are doing. A classic example of active learning is a think–pair–share discussion, in which a student thinks individually for a moment about a question posed on the lecture, then pairs up with a classmate to discuss their ideas, and subsequently shares their answer with the entire class (King, 1993). There are various modifications which can be incorporated into traditional lectures that enable active learning in the classroom, for instance; (a) the feedback lecture, which consists of two mini lectures separated by a small-group study session built around a study guide, and (b) the guided lecture, in which students listen to a 20- to 30-min presentation without taking notes, followed by their writing for 5 min on what they remember, and spending the remainder of the class duration in small groups for clarification and elaboration on the study material (Ellis, 2010; Johnson, 2013). Moreover, there are other active learning pedagogies, which include visual-based instruction (Johnson et al., 2016), small group problem based learning, cooperative learning, debates, drama, role playing and simulation and peer teaching. One innovative approach in education delivery system is the “flipped classroom,” an educational technique that consists of two parts, interactive group learning activities inside the classroom and direct personal computer-based individual instruction outside the classroom (Bishop & Verleger, 2013). As such, work typically done as homework in the didactic model (e.g., problem solving, essay writing) is better undertaken in class with the guidance of the teacher. Listening to a lecture or watching videos is undertaken at home. Hence, the term flipped or inverted classroom is used (Herreid & Schiller, 2013). The essence of a flipped classroom is that the activities carried out during traditional class time and self-study time are reversed or “flipped” (Veeramani, Madhugiri & Chand, 2015). Approaches to undergraduate teaching have improved over the years as the scholarship of learning and teaching has provided evidence of what works to improve the outcomes. However, educational delivery approaches have shown little change in many disciplines and have remained the same for the majority of the sectors (Van Vliet, Winnips & Brouwer, 2015). The flipped class is flexible itself and can be tailored (Tetreault, 2013). Historically, the concept of flipped classroom started in early 1990s. General Sylvanus Thayer created a system at West Point in USA, where a set of learning materials was given to engineering students so that they obtained core content prior to attending class. The classroom space was then used for critical thinking and group problem solving (Musallam, 2011). Many credited the rejuvenation of this idea with the development of, and increased access to, educational technologies (Moffett, 2015). For instance, the School of Business at the University of Miami proposed an ‘inverted classroom,’ which had events that traditionally took place inside the classroom now taking place outside the classroom and vice versa (Lage, Platt & Treglia, 2000). In 2000, a conference paper entitled ‘The Classroom Flip’ was presented by J Wesley Baker and the phrase ‘flipping the classroom’ was coined. Baker described how flipping the classroom could allow the trainer to become the ‘guide on the side’ rather than the ‘sage on the stage’ (Baker, 2000). In a sense, this reversal also flips the Bloom's revised taxonomy because the lower level of cognitive work/knowledge acquisition is done by the students, while educators work interactively with the students to develop the higher forms of cognition (Figure 1). To date, this approach has attracted a large amount of attention in the HPE and a subsequent surge of literature. A comparison between the traditional learning and the flipped classroom in the Bloom's taxonomy [Color figure can be viewed at wileyonlinelibrary.com] Fundamentally, a flipped classroom encompasses two established elements of education, the recorded lecture (off campus learning) and active learning (on campus learning). Lectures are given as homework, as an aid to learning. Homework is important because it is a time where students can share their learning progress with their family, reflect on their learning, and review the material as well as the educator's feedback (Fulton, 2012). The key characteristics of a flipped classroom compared to a traditional classroom and other existing teaching methods are summarised in Table 1. It has been highlighted that the flipped classroom fits into the broader context of blended learning (Tetreault, 2013). Blended learning as defined by Staker is ‘a formal education program in which a student learns at least in part through online delivery of content and instruction with some element of student control over time, place, path, and/or pace and at least in part at a supervised brick-and-mortar location away from home’(Staker & Horn, 2012, p.3). The flipped classroom consists of a formal education program, and online learning as a mechanism of informal learning through educational video quizzes/games. The flipped classroom approach is connected between what the students learn online (e.g., video lecture) and what they learn face-to-face (e.g., in-class active case study), and vice versa, which is a common feature of blended learning (Tetreault, 2013). In principle, the flipped classroom assigns relatively low-level cognitive learning such as memorising and understanding, outside of the classroom and teaching in class is accomplished mostly through teacher-student interactions and cooperation between peers, thereby stimulating the students’ intellectual potential (Rui et al., 2017). The option to view video lectures (as an example) outside of classroom has beneficial effects for the learners as they can replay the videos as many times as needed to better understand the key concepts at their own pace. Furthermore, this allows each student to be able to comprehend the topics being covered to his/her satisfaction, whereas this might not be possible in the context of conventional teacher-centred teaching. This is an important pedagogical consideration for international students for whom English is their second language (Moraros, Islam, Yu, Banow & Schindelka, 2015). From the teacher's perspective, a flipped classroom setting makes it easier to engage students and empower them as active participants of their own learning. There are several theoretical constructs that are applicable for a flipped classroom. Two of these include: the technology acceptance model (TAM) (Davis, 1989) and the unified theory of acceptance and use of technology (UTAUT) (Venkatesh, Morris, Davis & Davis, 2003). These theoretical constructs provide a framework for the analysis and identification of relevant outcomes. We will outline how these two theories of flipped classroom learning can improve the learning outcomes such as student satisfaction and improved scores. TAM includes two theoretical constructs: (a) perceived usefulness and (b) perceived ease of use. These constructs are defined as "the degree to which a person believes that using a particular system would enhance his or her job performance" and "the degree to which a person believes that using a particular system would be free of effort", respectively (Davis, 1989, p320). The first theoretical construct relies on students’ prior knowledge, gained from the pre-class video lecture (for example), in enhancing their understanding (and overall learning performance) in the active in-class activities such as problem solving. The second theoretical construct is based on students' perceptions that if a flipped class room is more user friendly than the traditional teaching mode, then they would be more likely to accept it. The goal of the UTAUT model is to explain the intentions of a user to use a given information system and the subsequent behaviour of the user. The model is based on four primary constructs: 1) performance expectancy, 2) effort expectancy, 3) social influence, and 4) facilitating conditions (Venkatesh et al., 2003, p447). The first three constructs reflect the motivation of the users (i.e., students). The fourth construct reflects the characteristics of a flipped classroom setup when students engage with the pre-class materials that are uploaded on an e-learning portal. These material could be a video, an interactive presentation, a questionnaire or sometimes a recorded audio. With regard to these theoretical constructs, if students perceive that a flipped class room is user friendly and the academic environment facilitates their learning, then it will promote students' engagement, interactions and cooperation in learning, which will further improve their performance. There are potential advantages of a flipped classroom, including increased opportunities to provide individualised education to learners (Johnson, 2013; Kachka, 2012), increased student engagement with course material (Gross, Pietri, Anderson, Moyano-Camihort & Graham, 2015), and increased educator-student interaction, compared to a ‘performing’ lecture. The Kirkpatrick model of educational outcomes (Barry Issenberg, McGaghie, Petrusa, Lee Gordon & Scalese, 2005; Kirkpatrick & Kirkpatrick, 1994) comprises ‘learners’ reaction’ (to the educational experience); learning (modification of attitudes/perceptions and the acquisition of knowledge and skills); behaviour (self-reported changes in practice and observed changes in practice, including new leadership positions); and results (which refers to change at the level of the organisation) (Figure 2). For instance, regarding the 'results' outcome, the flipped classroom allows the teacher to gain advanced, real-time insight into how students learn and quickly identify and better address curriculum content that the students find most challenging. This insight can be used to better inform decisions with regard to effective curriculum organisation, structure and the delivery of future classes. Four levels of learning in Kirkpatrick's model [Color figure can be viewed at wileyonlinelibrary.com] The success of a flipped classroom approach relies on a number of assumptions. Stimulation of students’ interest in learning and guided self-study (Moraros et al., 2015), primarily depends on the opportunities to actively engage students in self-directed learning and encourage progressive improvement (Bergmann, Overmyer & Wilie, 2012; Moraros et al., 2015) in assessment performances. Thus, a flipped class will not support effective learning if students fail to engage with the assigned pre-class or in-class activities (Kachka, 2012), for reasons which might include poorly designed educational materials (e.g., long, poor audio quality) or students feeling ‘lost’ (Moffett, 2015). As such, a number of contextual and structural factors that can influence flipped classroom learning include resources (inputs to the program), activities (aspects of implementation), outputs (observable products of the completed activities) and outcomes (effects or impacts within various time frames) as depicted in the conceptual framework (Figure 3). Logic model of flipped class learning [Color figure can be viewed at wileyonlinelibrary.com] There are individual studies, which have evaluated flipped classroom in medical education, allied health education and health science education, using a pre-and post-test design or comparative designs to explore how learning outcomes are improved. Some studies showed positive outcomes with flipped classroom (Galway, Corbett, Takaro, Tairyan & Frank, 2014; Van Vliet et al., 2015), while others showed the opposite (Whillier & Lystad, 2015). For instance, a study on integrated flipped lectures with online teaching techniques assessed learning experiences and participation through active learning. The findings suggested that the students in the integrated flipped-online lectures had achieved an increase in active learning components compared to the group that were put in a didactic model (Galway et al., 2014). It is important to understand the factors that could have contributed to this difference. As an example, for balance of the safe learning environment (to be free from discomfort and fear) between the two groups of students, a comparability of the personality traits between the students in each group needs to be considered. On the other hand, another individual study, which assessed the effectiveness of flipped classroom in ophthalmology clerkship reported that the students in flipped classroom had more burden and pressure in preparing for the pre-class compared with the students in lecturer-based classroom group. Thus far, these published individual studies varied in design, sample size and outcome measures. It is unclear, if these findings would be generalised to other HPE. A non-Campbell systematic review of the flipped classroom reported how the flipped classroom has been applied in nursing education and the achieved outcomes associated with such teaching (Betihavas, Bridgman, Kornhaber & Cross, 2016). Due to the focus on a particular educational context (i.e., nursing or ophthalmology), the generalisability of their findings to other courses in undergraduate HPE is uncertain. Another non-Campbell collaborative systematic review, consisting of 82 studies reported on the effectiveness of flipped classroom in medical education where a pooled estimate of a subset of six experimental studies showed generally positive perceptions of the students to the flipped classroom. However, there were no significant changes in knowledge and skills (Cohen's d = −0.27 to 1.21, median: 0.08; Chen, Lui, & Martinelli, 2017). These systematic reviews, focused on a particular area (either nursing education or medical education) had a limited number of included studies, considerable variation in study designs, a lack of methodological quality assessment of the included studies, and the quality of evidence reported by these systematic reviews is poor. A systematic review which combines the results of interventions, using flipped classroom compared with alternative learning or traditional learning, will help us to make recommendations for the development and implementation of successful flipped classroom amongst health professionals. The current review also aims to serve as a reference for decision makers to support evidence-based approaches to flipped classroom in HPE. The primary objective of this systematic review is to assess the effectiveness of flipped classroom intervention for undergraduate health professional students on academic performance and course satisfaction. The influence of context in the design, delivery and outcomes of the flipped classroom interventions in undergraduate health professional education; The barriers and facilitators of flipped classroom learning effectiveness for undergraduate health professional students. Specifically, this review is designed to answer the following research questions: What are the effects of flipped classroom learning on undergraduate health professional students' academic performance? What are the effects of flipped classroom learning on undergraduate health professional students' course satisfaction? Do any moderator variables affect the effectiveness of flipped classroom learning on academic performance outcomes? Moderators will include (if data are available), study design, student related factors such as the amount of out-of-class preparation time, classroom availability and limited high speed internet access for rural and remote students, quality of interactive tools, and faculty related factors such as faculty members' preference to a more didactic approach. Randomised designs, which include individual-level randomised trials, cluster-level randomised trials and natural experiments, where assignment to treatment or control conditions is functionally random. Non-randomised designs, which include at least one treatment group and at least one comparison group, matching designs, two-group pre-post designs, regression discontinuity designs. We do not include qualitative research. We included all undergraduate health professional students, regardless of the type of healthcare streams (e.g., medicine, dentistry, nursing, pharmacy), duration of the learning activity (e.g., one or two semesters) or the country where the study is conducted. Any educational intervention that includes the flipped classroom as a teaching and learning activity in undergraduate programmes, regardless of the type of healthcare streams (e.g., medicine, dentistry, nursing, pharmacy) will be considered. To be included, a study must explicitly indicate that the teaching/learning activities for undergraduate students included in the flipped classroom, reversed classroom or flipping class, aiming to improve student learning and/or student satisfaction. Standard lectures and subsequent tutorial formats will not be considered as flipped classroom. Studies on flipped classroom methods among undergraduate or postgraduate students who are not from the healthcare streams (e.g., engineering, economics, computer science) will be excluded. We explored the impact of flipped classroom learning in undergraduate health professional students on academic related outcomes. The primary outcome is academic performance measured by examination scores, final grades or other formal assessment methods at immediate post-test. The secondary outcome is student satisfaction measured at immediate post-test using a self report scale, which may include the training institution's own format of assessing student satisfaction. 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In K–12 and higher educational circles, the “flipped classroom” instructional strategy (also known as the “inverted classroom”) has been receiving a lot of attention. The idea is that rather than taking up limited class time for an instructor to introduce a concept (often via lecture), the instructor can create a video lecture, screencast, or vodcast that teaches students the concept, freeing up valuable class time for more engaging (and often collaborative) activities typically facilitated by the instructor. It is important to note that the strategy should involve more than just the “take home” video lecture (or screencast or podcast). It should also incorporate formative and summative assessment, as well as meaningful face-to-face (F2F) learning activities. Although many instructors at all educational levels and from various settings have been incorporating this strategy for years, the term is most often attributed to two Colorado high school teachers, Jonathan Bergman and Aaron Sams, who began creating screencasts and podcasts for their students in 2006 (Makice, 2012).The flipped classroom strategy advocates tout numerous benefits. Most seem to be plausible advantages (e.g., increases time for more engaging instruction), especially for those teaching in hybrid or blended settings consisting of some combination of F2F and online instruction; however, the strategy also has its limitations. First, the quality of the video lecture may be very poor; even though an instructor might be outstanding in F2F settings, he or she may not produce a quality video instructionally and/or technically. Second, taking for granted that all students are able to view the video lecture on their own computers, the conditions under which they might view the video may not be the best for learning any concepts (e.g., a student might view a video while also watching a baseball game and listening to music). Arguably, there are many distractions in F2F classrooms, but at least the teacher can monitor comprehension with several formative assessments. Third, students may not watch or comprehend the video and therefore be unprepared or insufficiently prepared for the more engaging activities that will occur F2F. Fourth, students may need a lot of scaffolding to ensure they understand the material presented in the video. Although good instructors will likely build-in effective scaffolding activities while students watch the video such as “stop, think, and answer” questions (and also rewind if needed), they may still fall short in providing enough scaffolding activities for all types of learners. Fifth, students are not able to ask questions of the instructor or their peers if they watch the video alone. Therefore, important just-in-time questions to help them comprehend the material cannot occur unless the instructor is available during the viewing—which is difficult. Finally, the flipped classroom strategy may not be the best approach for second language learners or those with learning challenges—which represents learners not only at the K–12 level, but all educational levels and settings.Although there are many limitations to the flipped classroom strategy and no empirical research exists to substantiate its use, anecdotal reports by many instructors maintain that it can be used as a valuable teaching strategy at any educational level, depending on one’s learners, resources, and time. Moreover, it seems to be a good fit for teaching knowledge that is procedural, one of the four general types of knowledge described in the revised Bloom’s Taxonomy (Anderson et al., 2001). Procedural knowledge is knowledge about how to do something. Therefore, a flipped classroom video lecture about how to solve a quadratic equation in which an instructor describes and models how to solve this type of problem would be a good use of the strategy. Complex procedural knowledge can also be taught utilizing the flipped classroom strategy although scaffolding and chunking of content will be very important not only to ensure that videos are short, but also to make certain that all of the steps of the procedure are introduced adequately so students understand it thoroughly.Although procedural knowledge is arguably the best type of knowledge to teach using the flipped classroom strategy, the other three types of knowledge— factual (knowledge describing the basic and essential elements a person must know), conceptual (knowledge of the relationship between classifications and categories), and metacognitive knowledge (knowledge about one’s own cognition)— can also be taught using this strategy. However, it is important to note that much more time and thought will need to go into employing the flipped classroom strategy.Many resources exist regarding the flipped classroom strategy. A few are:

Online learning is using the Internet to provide educational information, resources, and assistance to students, allowing meaningful interaction and supporting knowledge creation. This study used the descriptive-correlational research design to determine the predictors of students’ academic performance in online learning at the College of Education of Misamis University, Ozamiz City. Stratified random sampling was used to get the 124 education students who served as respondents. An adopted instrument in the Factors that Affect Students’ Academic Performance in Online Learning questionnaire from Gopal and Mushtaq (2020) consisting of 28 statements was used as the research instrument. This tool consists of 4 categories: Study Habits, Teacher Skills and Efforts, Accessibility, and Parental Involvement. Results showed that the perceived factors influencing students’ academic performance in online learning regarding study habits, teacher skills and efforts, accessibility, and parental involvement were high. Students have average performance in online learning based on their general weighted average in the first semester of the school year 2021-2022. Study habits and accessibility were significantly correlated with academic performance. The predictors of students’ academic performance in online learning were study habits and accessibility. Students with good study habits and internet connectivity will likely achieve and maintain good grades in their online learning. If internet access is not widely available in rural areas, teachers give extensions to pass requirements without deducting more significant points.

Background and purpose of the studySTEM learning often involves a multitude of complex and abstract concepts and ideas that can be challenging for students to comprehend. Research suggests that the oral and visual representations in video lectures can maximize students’ cognitive infrastructure, helping them to organize knowledge more effectively. However, compared to traditional learning methods, video lectures may lack interaction and feedback, which can lead to ineffective learning strategies (e.g., passive viewing) and reduced learning engagement. Instructional explanations serve as a generative strategy, enabling students to create oral and written pieces based on the knowledge gained from video lectures and their prior knowledge. This study recruited a total of 87 undergraduate students and explored how the modality of instructional explanations generated by these students for a fictious student influenced their learning. Specifically, the study explored the effects on students’ learning performance, attention, behavioral patterns of preparing-to-explain, the quality of notes, and the quality of instructional explanations in video lectures on a STEM subject.ResultsThe results revealed that students who adopted a combination of oral and written instructional explanations showed better immediate retention and transfer than those adopted just one type of explanation. In addition, both oral-only and combined oral-and-written explanations promoted more self-regulated learning behaviors during the phase of preparing-to-explain. The study also found that the quality of instructional explanations played a mediating role in the effects of modality.Conclusions and potential implicationsOur findings suggest that combining oral and written instructional explanations is more effective in supporting students’ STEM learning from video lectures compared to using a single form of explanation. These findings have significant implications for teaching and learning STEM subjects through video lectures. Students and educators should recognize the complementary roles of oral and written instructional explanations and opt for a combined oral-and-written approach during STEM learning activities.

Learning from video lectures is becoming a prevalent learning activity in formal and informal settings. However, relatively little research has been carried out on the interactions of learning strategies and social environment in learning from video lectures. The present study addresses this gap by examining whether learner-generated explanations and co-learner presence with or without nonverbal praise independently and interactively affected learning from a self-paced video lecture about infectious diseases. University students were randomized into viewing either the video with instructor-generated explanations or the same video but generating explanations themselves. Outcomes were assessed by the quality of explanations, learning performance, mental effort, attention allocation, and behavioral patterns. Between-group comparisons showed that, in the absence of a peer co-learner, learning performance was similar in both the instructor-generated and learner-generated explanation groups. However, in the presence of a peer, learner-generated explanation facilitated learning performance. Furthermore, learner-generated explanation in the presence of a co-learner also reduced learners’ mental effort and primed more behaviors related to self-regulation and monitoring. The results lead to the following strong recommendation for educational practice when using video lectures: if students learn by generating their own explanations in the presence of a co-learner, they will show better learning performance even though the learning is not necessarily more demanding, and will engage in more behaviors related to explanation adjustment and self-regulation.