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Nikolopoulou, K.; Zacharis, G. Blended Learning in a Higher Education. Encyclopedia. Available online: https://encyclopedia.pub/entry/44774 (accessed on 13 June 2024).
Nikolopoulou K, Zacharis G. Blended Learning in a Higher Education. Encyclopedia. Available at: https://encyclopedia.pub/entry/44774. Accessed June 13, 2024.
Nikolopoulou, Kleopatra, Georgios Zacharis. "Blended Learning in a Higher Education" Encyclopedia, https://encyclopedia.pub/entry/44774 (accessed June 13, 2024).
Nikolopoulou, K., & Zacharis, G. (2023, May 24). Blended Learning in a Higher Education. In Encyclopedia. https://encyclopedia.pub/entry/44774
Nikolopoulou, Kleopatra and Georgios Zacharis. "Blended Learning in a Higher Education." Encyclopedia. Web. 24 May, 2023.
Blended Learning in a Higher Education
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Blended learning is a growing phenomenon in higher education after the COVID-19 pandemic (the educational process moved entirely online), and the way is prepared for blended education mode in universities. Although blended learning research is on the rise, fewer studies regard university students’ learning behavior in blended learning environments. 

blended learning university higher education

1. Introduction

After the COVID-19 pandemic blended modes of education, and online education increasingly spread at universities and higher education institutions [1][2][3], many university students express their preference towards blended approaches of teaching and learning [3][4]. Blended or hybrid education takes place partially on the Internet; it may be that some students attend the lesson in person while (at the same time) others are online, or some lessons take place face-to-face and some take place online [5]. Blended learning “is not a simple juxtaposition of physical presence and technology mediation, but a well-studied alternation of the two, aiming to make the most of the various components and design effective work contexts for both students and teachers” [6], p. 1. Blended learning approaches in higher education combine the flexibility and convenience of online courses with in-person interactions [7] and are associated with benefits such as flexible learning [8] and improved student self-regulated/directed learning [9][10].
After the pandemic, the application of blended learning mode is growing/increasing, while university students studying different academic subjects learn via blended learning approaches, and they are considered as new practitioners [11]; that is, students began engaging with online and blended learning during the pandemic period, without having any previous experience. Research evidence on pandemic-related blended learning in higher education reported on different issues, such as academics’ perspectives [12]; professional development initiatives/opportunities [13]; student self-regulation strategies [14]; students’ views of blended learning to develop learner autonomy [15]; influence of blended learning on students’ learning responsibility, motivation, and involvement [15]; and different factors (e.g., e-learning environment, materials, technical support, interaction with instructors and peer students) that may affect blended learning effectiveness [16]. Digital technology (Information and Communication Technologies) is a basic element of blended learning, since the flexibility and ability of learners to access educational resources/activities via the Internet constitutes an advantage. A review [9] suggested that effective online and blended education requires, among other things, students with high self-regulation skills and sufficient digital literacy, as well as a high sense of belonging; the student is considered as a main factor besides the course, the teacher, and the institution. Although there is a growing number of research evidence on blended learning from March 2020 onwards, fewer studies regard university students’ learning behavior/perceptions in blended learning environments [15][17][18].

2. University Students’ Learning Behavior Perceptions

It is acknowledged that blended learning was used prior to COVID-19; it is not new at the university level [19][20]. For example, a study dating back to 2011 [19] investigated university students’ attitudes towards blended learning, and more specifically students’ perceptions before and after actual system use; the e-learning system was well accepted. Another study in 2014 [20] investigated student engagement and blended approaches to learning in higher education; it was indicated that collaborative learning applications and a blended approach can be used to design and support assessment activities that increase levels of student engagement. However, the pandemic is considered as a turning point for blended mode in universities; the rise of blended approaches and online education appeared as a consequence of the pandemic [1]. Blended education was a novel mode of education for university students who traditionally study in face-to-face mode; during the COVID-19 pandemic, online/blended education was offered as a response to an emergency.
For these reasons, all studies regard university students’ learning behavior perceptions during or after the COVID-19 pandemic; the focus is on recent studies published in 2021 and 2022. As student motivation is particularly relevant in blended learning environments, a brief definition is provided. Student motivation to learn regards student willingness to attend university lectures/classes and can be intrinsic or extrinsic; intrinsically motivated students participate in the learning process for the pleasure/satisfaction they get from it, while extrinsic motivation regards carrying out an activity out of an obligation [21] (students expect certain gains such as obtaining certificates or better marks [22]).
In China, within the context of a university translation course, blended learning was applied [15]. Quantitative data (through a questionnaire) and qualitative data (via interviews) were collected from 120 students. Different aspects such as students’ perceptions of blended learning to develop learner autonomy, teachers’ construction of a blended course, and the influence of blended learning on students’ learning responsibility, motivation, and involvement were explored. According to the findings, most students perceive blended learning as an effective way to develop learner autonomy; they are learners of quite high levels of learning involvement, motivation, and responsibility (their learning independence is also moderately high).
Ballouk et al. [17], in Australia, explored the way medical students learn in a program that applies a blended learning strategy. They developed and validated an instrument which revealed learning, motivation, and delivery of content as major groups. Motivation and resources influence students’ learning behavior and study habits, while learning was associated with the social context (the role of learning with others/peers). Researchers used the instrument that had been previously validated by Ballouk et al. [17], and this is discussed in the research instrument section. However, the questionnaire was administered to a different country and culture, to students studying different academic subjects.
In Saudi Arabia, Al-Kahtani et al. [7] implemented a longitudinal study (2018–2021) with 30 health science students, and found increased student satisfaction, engagement, convenience, and enhanced learning during the period following the pandemic lockdowns (adaptation period). The majority of the sample reported that such online and blended education allowed them to understand basic concepts, while students attending the blended-mode group indicated a higher achievement. In the same country, a study at the beginning of the pandemic [23] collected qualitative data from 12 students, in order to explore EFL students’ perceived benefits and challenges of blended learning during the spread of COVID-19. EFL (English as a foreign language) students’ perceived benefits of blended learning include support of their writing skills and utilizing online resources to search for various topics. In parallel, perceived challenges include technological problems, difficulties with online tests, and the university council’s decisions.
Another researcher, in the Maldives [24], administered a questionnaire to 407 university students from different academic disciplines and reported students’ positive perceptions about blended learning; most participants were receptive to the use of technology for learning. Increased access to learning and flexibility were perceived as major benefits, while barriers included limited internet infrastructure and technical support. The study reported mixed views regarding enhanced learner engagement (41.4% believe blended learning does not provide them with more learner engagement), while some differences were identified in relation to academic disciplines; e.g., students studying tourism and business subjects were more negative about blended learning in comparison to those studying science and engineering.
With regard to students’ perceived benefits of blended education, these are frequently associated with combining the benefits of online and face-to-face education (e.g., socializing with peers during the implementation of in-person approaches and being more autonomous and self-directed in home environments) [2][25]. Another benefit regards the application of practical sessions (e.g., practical/lab work) after the theory [26]. Finlay et al. [26], in the UK, investigated the views and experiences of undergraduate sport and exercise science students with regard to online and blended learning strategies during the pandemic. Blended learning was shown to have a higher overall course satisfaction score (e.g., with regard to learning resources, academic support, feedback, learning opportunities, and assessment); students’ clear preference for blended learning reveals that students appreciate the access to in-person classes. Some differences within the same-year group regard assessment and feedback, academic support, and learning community, with higher perception scores reported in the blended learning survey (vs. the online learning survey). Similarly, in another study with student teachers in South Africa [27], the in-person aspect (physical presence) of blended learning eliminated the challenge of a digital divide (existent in fully online teaching), and students exercised self-directed learning skills such as identifying resources and learning goals, being responsible for their learning, critical thinking, and collaboration and problem-solving skills.
With regard to student engagement and motivation (to improve students’ academic performance with online/blended classes), this was reported as a challenging issue during the pandemic period [28][29]. Students’ perceptions of the factors that influence their interest and motivation for engagement include teacher and teaching methods [30][31], the academic discipline [30], and online activities [2]. For example, students were demotivated when tutors did not support them (e.g., through providing feedback) during online learning [31]. In parallel, a small number of studies indicated differentiation of student perceptions with regard to demographic characteristics (e.g., gender). For example, a study in Bahrain and Saudi Arabia [16] explored the relationship of eight independent factors: e-learning environment, e-learning facilitation, e-learning materials, e-learning technical support, instructors’ personal attention, interaction with instructors, interaction with peer students, and laboratory learning environment, in the provision of effective blended learning in higher education during the pandemic. Undergraduate and postgraduate students’ perceptions of blended learning effectiveness in universities differed with regard to gender (female students utilize online teaching and learning to the maximum, while male students get more benefits in face-to-face discussions) and level of the course (younger students are well versed in digital competency) [16].
The study differs from the above-mentioned studies in that researchers focused on university students studying various academic subjects using a validated instrument. Researchers also examined the effect of specific demographic variables (gender, year of study, age, faculty) on students’ blended learning behavior perceptions. The majority of the aforementioned studies were carried out with students attending a specific course or program (e.g., health sciences, English as a foreign language).
In Greece, there is a small number of studies regarding students’ learning behavior perceptions in blended learning contexts. A study [32] conducted immediately after students returned back to face-to-face education indicated that they intend to use e-learning platforms to learn in the post-pandemic era. Researchers used the Unified Theory of Acceptance and Use of Technology (UTAUT2 model extended with the construct ‘Learning Value’) to determine the factors predicting university students’ behavioral intention to use e-learning platforms in the post-pandemic era; students’ acceptance of e-learning platforms is critical for the success of online/blended learning. It was found that the variables Performance Expectancy, Social Influence, Hedonic Motivation, Learning Value, and Habit had a significant impact on students’ intention to use e-learning platforms to learn, while Facilitating Conditions and Learning Value had a direct impact on actual use. Greek students also expressed preference for both in-person and hybrid approaches for learning in the future, and their positive blended learning perspectives were associated with the combination of benefits offered via in-person and online education [3].

3. Descriptive Measures for Students’ Learning Behavior Perceptions

A descriptive analysis was applied in order to investigate students’ learning behavior perceptions in a blended learning environment. Table 1 shows students’ response percentage frequencies on the 19 items of the questionnaire (N = 176 students). The last column of the table has added together the percentages of those who “agree” and “strongly agree”. The majority of the students expressed strong learning behavior perceptions. More specifically, over 77% of the sample “agree and strongly agree” with items S4, S17, S15, S7, S14, S16, S1, S19, and S6. The items with the highest percentages of agreement were S4 (agreement 97.7%) and S17(agreement 88.7%); these items regard the importance and efficiency of audio-visual online resources in learning, characteristics that can motivate students’ learning. Examples of items with lower percentages of agreement (and higher percentages of uncertainty) were S10, for which 50% of the students agree that their study habits are affected by their peers/social interaction (25% undecided/uncertain); and S3, for which 54% express the view they are able to consolidate their learning following a small group activity (38% undecided/uncertain). Such perceptions are related to student motivation and learning in blended environments and have implications for student training.
Table 1. Students’ response percentage frequencies on the 19 items (N = 176 students).
  SD D U A SA A & SA
S4. I find external audio-visual online resources very important to my learning 0.0 0.0 2.3 54.5 43.2 97.7
S17. Some online resources are efficient because they are well summarized 0.0 0.0 11.3 52.3 36.4 88.7
S15. Access to online material off-campus enables me to structure my independent learning 0.0 4.5 9.1 50.0 36.4 86.4
S7. My use of study resources differs leading up to exams 2.3 2.3 9.1 29.5 56.8 86.3
S14. I learn more efficiently when I’m able to access online resources using different devices 0.0 4.5 11.4 45.5 38.6 84.1
S16. I use Faculty lecture material as a guide for what to learn 0.0 4.5 11.4 43.2 40.9 84.1
S1. I actively seek online resources to prepare my learning materials before a learning activity (tutorial/lecture/ward presentation) 0.0 4.5 11.4 50.0 34.1 84.1
S19. I often integrate a variety of Faculty and external online resources to support my learning 0.0 0.0 18.2 47.7 34.1 81.8
S6. Flexibility to use a variety of online material motivates my independent learning 0.0 4.5 18.2 47.8 29.5 77.3
S11. I set up study goals that organise/structure my learning 0.0 9.1 25.0 43.2 22.7 65.9
S13. Accessibility to Faculty lectures online enhances my independent learning 0.0 9.1 25.0 43.2 22.7 65.9
S2. I find small group work enhances my understanding about a particular concept 2.3 4.5 31.8 38.7 22.7 61.4
S18. Specific external online resources are vital to my independent learning 0.0 4.5 36.4 31.8 27.3 59.1
S9. My study is stimulated by group discussions 4.5 4.5 36.4 43.2 11.4 54.6
S5. I find the audio-visual online resources provided by the Faculty crucial for my learning 0.0 9.1 36.4 34.0 20.5 54.5
S3. I am able to consolidate my learning following a small group activity 2.3 4.5 38.7 38.6 15.9 54.5
S8. My motivation to study increases leading up to exams 4.5 11.4 31.8 25.0 27.3 52.3
S10. My study habits are influenced by my peers/social interaction 4.5 20.5 25.0 40.9 9.1 50.0
S12. My study is influenced by the fact that I need to maintain my image (among peers/supervisors) 6.8 27.3 27.3 25.0 13.6 38.6
(SD = Strongly Disagree, D = Disagree, U = Undecided/not sure, A = Agree, SA = Strongly Agree).

4. Factorial Structure of the Questionnaire

For the exploration of the factorial validity of the questionnaire, an exploratory factor analysis was performed using the Principal Axis Factoring method accompanied by the Oblimin Factor rotation method. The items S5, S7, and S13 were eliminated due to cross loading, while the item S10 was taken out due to low loading (<0.4). For the remaining 15 items, the Kaiser–Meyer–Olkin (KMO) test for sampling adequacy and Bartlett’s test for sphericity were used; KMO was used because it is the standard measure to support sampling adequacy, indicating that the items meet the conditions for factor analysis [33]. The KMO measure (KMO =0.619) indicates adequacy with a value greater than the cutoff for adequacy (0.5). Bartlett’s test indicated a very good sphericity (v2 = 1199.937, df = 105, p < 0.001). The Scree Plot supports a three-factor solution which researchers retain for interpretation. Factor number one (F1), named “Resources”, was linked to seven statements: S15, S14, S4, S18, S6, S19, S1. Factor number two (F2), named “Learning”, was linked to three statements: S2, S3, and S9. Factor number three (F3), named “Motivation”, was linked to five statements: S11, S12, S8, S16, and S17. Factor loadings were above 0.4 and no items were candidates for elimination [34].

5. Impact of Characteristics on Blended Learning Behavior Perception Factors

To identify the possible impact of students’ characteristics (gender, year of study, age, and faculty) on their blended learning behavior perceptions, one-way ANOVA analyses were performed. A significance of p = 0.05 was accepted as a conventional level. Regarding gender, there was a statistically significant difference for all three factors (F1: Resources, F2: Learning, F3: Motivation); male students expressed more positive perceptions for F1 (F (1, 174) = 9.63, p = 0.002) and F2 (F (1, 174) = 9.45, p = 0.002), while female students did so for F3 (F (1, 174) = 9.21, p = 0.003). Similarly, regarding year of study, a statistically significant difference occurred for all three factors; first-year students expressed more positive perceptions for F2 (F (2, 173) = 7.66, p = 0.000), third-year students had more positive perceptions for F3 (F (2, 173) = 4.01, p = 0.000), while fourth-year students and above expressed more positive perceptions for F1 (F (2, 173) = 3.62, p = 0.025). Pairwise mean comparisons using the Bonferroni test [35] did not reveal significant differences between year of study and Resources (F1). For F2, students in their third year of study expressed more positive perspectives in comparison to the other years. Regarding F3, those in their third year of study or above had more positive perceptions in comparison to first year students.
Regarding age group, no statistically significant difference occurred. Regarding faculty, a statistically significant difference occurred for all three factors; health sciences students expressed more positive beliefs for F1 (F (3, 172) = 3.62, p = 0.014) and F3 (F (3, 172) = 13.35, p = 0.000), while applied sciences students expressed more positive beliefs for F2 (F (3, 172) = 3.05, p = 0.000). Pairwise mean comparisons using the Bonferroni test revealed differences for all three factors. Students studying economics or computer science expressed more positive perceptions in comparison to those studying applied science (for F1) and in comparison to all other faculties for F3. For F2, those studying applied sciences expressed higher perceptions in comparison to those studying humanities or social sciences.

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