In recent years, higher education institutions have increasingly adopted hybrid learning models that combine in-person and distance instruction (Author2 et al., 2024). This shift has required adaptation to new technological, pedagogical, and social learning environments. While distance learning provides flexibility, autonomy, and time efficiency, it also presents challenges such as screen fatigue, technical barriers, isolation, and lower engagement (Salta et al., 2022). Recent research, enhanced by AI-based tools, explores learning processes, collaboration, and innovative teaching (Castellanos-Reyes et al., 2025). Many studies underscore that effective distance learning requires self-regulated learning, innovative pedagogy, and strong academic self-efficacy (Yoo & Jung, 2022). However, little attention has been given to what occurs behind the scenes of distance learning, teaching, and assessment.

Distance Learning and Teaching in Higher Education

The pandemic forced higher education systems worldwide into fully remote learning. Research on this transition revealed student dissatisfaction with fully remote learning, citing stress, isolation, and prolonged screen time (Shraga-Roitman et al., 2022). First-year students found distance learning less effective than advanced students (Stevanovic et al., 2021). Gender differences were also reported: female students demonstrated greater adaptability, self-regulation, and more positive attitudes than males (Aristovnik et al., 2020; Author2 et al., 2024). Students with specific learning disorders (SLD) or ADHD expressed lower satisfaction and engagement (Sarid & Lipka, 2024).

Concerns about the quality and sustainability of distance learning persist, particularly regarding assessment practices (Toumpalidou & Konstatoolaki, 2023). To address these concerns, researchers stress the need for investment in developing students’ digital skills and self-efficacy (Author2 et al., 2024).

Academic Self-Efficacy (ASE) and Distance Learning Efficacy

Academic self-efficacy (ASE) refers to an individual’s belief in their ability to meet academic challenges (Bandura, 2018). High ASE correlates with academic achievement, adaptation, satisfaction, and engagement in distance learning (Sarid & Lipka, 2024). Building ASE requires persistence and resilience. Students with SLD or ADHD consistently report lower ASE than peers without disabilities, both before and after the transition to distance learning (Mana et al., 2022). In online environments, distance learning self-efficacy is an important subdomain of ASE. It predicts students’ conceptions of learning, social presence, and epistemological beliefs (Yavuzalp & Bancivan, 2021). Engaging students in active, problem-oriented learning, such as formulating questions or designing solutions, enhances both efficacy and wellbeing (Vilhunen et al., 2025).

The current study was conducted at a college of education. The study aimed to explore students’ perceptions of distance learning, particularly their views of the “behind-the-scenes” activities: actions occurring during online lessons but not directly related to instruction (e.g., chatting, multitasking, browsing). This research offers a perspective contrasting students’ experiences and examining both explicit (pedagogical) and implicit (behavioral, social) aspects of online education. Understanding these dynamics can guide institutions seeking to enhance hybrid learning and decision-making processes.

Based on existing literature, four directional hypotheses were proposed:

H1. Advanced-year students will show stronger preference and higher self-efficacy for distance learning than first-year students (Stevanovic et al., 2021).
H2. Female students will report higher preference and self-efficacy for distance learning than males (Aristovnik, 2020; Author2 et al., 2024).
H3. Students with SLD or ADHD will report lower preference, lower distance learning self-efficacy, and lower ASE than students without disabilities (Sarid & Lipka, 2024).
H4. ASE, distance learning/teaching self-efficacy, and lecturer support will predict preference for distance learning among students, beyond demographic variables (Sarid & Lipka, 2024).

The study included a convenience sample of 242 students enrolled in bachelor’s and master’s degree programs at a teacher education college. The participant pool exhibited heterogeneity across several key demographics and learning profiles. A large majority of participants were female (86% in the bachelor’s program and 78% in the master’s program). The average age differed substantially between the groups, with bachelor’s students having a mean age of 27 years (SD = 7.23) and master’s students having a mean age of 42 years (SD = 9.92). Regarding learning profiles, 49% of bachelor’s students and 24% of master’s students reported having a diagnosed Specific Learning Disorder (SLD) or ADHD. The study employed a multi-instrument approach comprising two main questionnaires: 1. Distance Learning Perception and Preference Questionnaire (DLPQ): This instrument was developed specifically for this research to assess several core constructs related to online learning. Key scales included: o Distance Learning Preference: Measuring students' overall preference for online instruction versus face-to-face instruction. o Perceived Proportion of Online Instruction: Assessing the proportion of online instruction students preferred to have in their programs. o Distance Learning Self-Efficacy: Measuring students’ confidence in successfully completing online learning tasks. o Behind-the-Scenes Activities (BTS): Scales measuring the perceived scope (frequency) of off-task behaviors during online lessons (e.g., private messaging, social media use, browsing) and the perceived negative influence of these activities on their academic performance. o Lecturer Support: Assessing students' perceptions of the level of instructional and interpersonal support received from their lecturers during online modules. 2. Academic Self-Efficacy Scale (ASES): The established scale developed by Zimmerman, Bandura, & Martinez-Pons (1992) was utilized to measure general Academic Self-Efficacy (ASE), assessing students' belief in their ability to meet broad academic challenges. Data was collected using an online format (questionnaires) distributed to students by the college’s Research Authority. Prior to participation, students received detailed information about the study’s purpose, assurance of full anonymity and confidentiality, and provided voluntary consent in accordance with institutional ethical guidelines. Data analysis involved descriptive Statistics to report general preferences and frequencies; Inferential Statistics to examine subgroup differences across demographic variables (sex, year of study, SLD/ADHD presence) concerning ASE, distance learning efficacy, and preference; and Hierarchical Multiple Regression Analysis was used to identifying the predictors of students' preference for distance learning, controlling for demographic variables.

This study provides crucial empirical evidence regarding students’ perceptions of distance learning, moving beyond traditional metrics to explore the nuanced behavioral and psychological realities of the hybrid classroom. Students generally expressed a favorable and pragmatic view of distance learning, valuing its flexibility and autonomy. They reported a moderate level of behind-the-scenes activity (multitasking, browsing) but perceived its overall impact on their academic success as minimal, suggesting a potential normalization of these behaviors in modern digital learning or possibly an overconfidence in their ability to multitask. The most significant conclusion is the established role of distance learning efficacy as the primary determinant of preference for online instruction. The finding that this specific efficacy belief overshadowed general Academic Self-Efficacy and demographic variables highlights the need to focus interventions specifically on building students' confidence in navigating the practical and technological demands of remote learning. Subgroup differences emphasize the need for differentiated strategies. The higher preference and efficacy found among female and advanced students suggest that institutions should provide targeted support for first-year students and male students to enhance their adaptation. Furthermore, while students with SLD/ADHD reported lower general ASE, the fact that their preference for distance learning did not differ significantly from their peers suggests that the online format may offer compensatory benefits (e.g., self-paced engagement, reduced social pressure) that should be strategically utilized in inclusive course design. Ultimately, the study confirms that the success of hybrid education depends not solely on technological readiness but critically on relational and psychological factors. Fostering Academic Self-Efficacy, enhancing perceived lecturer support, and designing interactive environments are essential strategies to mitigate disengagement and ensure the psychological sustainability of hybrid learning models in contemporary higher education.

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