First-Year Students’ Motivations and Self-Regulated Learning Strategies in Online Study

2.1 Motivation for choosing online studies

Prior research has identified a range of motivations for choosing online learning programs. Stephani et al. (2023) categorized students’ motivations into four key domains: flexibility, learning, interaction, and expression. Flexibility refers to the ability to study at one’s own pace and manage learning around work or personal commitments, often reducing the time and cost related to commuting or relocation. Learning-related motivations include the desire for academic or professional development without interrupting current employment. Interaction encompasses opportunities to build networks and engage with peers across geographical boundaries. Finally, expression captures how the online format may support students—particularly those who are more introverted—in sharing their perspectives in psychologically safe environments.

2.2 First-year students in online higher education

For students entering fully online programs, the transition into higher education involves a double shift: adapting to higher education while simultaneously adapting to distance learning. Research suggests that many first-time distance learners underestimate the time demands of online study while overestimating the flexibility, and that integrating the new demands of studying into their existing lives is a common challenge (Brown et al., 2015; Schweighart et al., 2024). Brown et al. (2015) further found that around three quarters of first-semester distance learners adopted a “lone wolf” approach to their studies, avoiding contact with teachers and peers, which made it harder to seek help and maintain motivation. Most existing research in this field has, however, focused on adult learners balancing education with work and family. The growing group of younger students entering online programs directly from upper secondary school—without the life experience and self-directedness that adult learner research typically assumes—remains a largely understudied population (Korstange et al., 2020; Schweighart et al., 2024).

2.3 Self-regulated learning

Self-regulated learning (SRL) refers to students’ abilities to actively plan, monitor, and regulate their own learning processes, including their motivation, behavior, and cognition. It is not merely a mental ability or an academic performance skill; rather, it is the self-directive process by which learners transform their mental abilities into academic skills (Zimmerman, 2002). Zimmerman describes SRL as a cyclical process involving three phases: forethought, performance, and self-reflection. In the forethought phase, students set specific goals and plan strategies for achieving them. During the performance phase, they deploy these strategies while self-observing their progress. In the self-reflection phase, they evaluate their outcomes and adjust their methods based on self-assessment rather than external feedback alone. Across all three phases, motivational beliefs such as self-efficacy and intrinsic task interest play a central role in sustaining engagement and persistence. SRL also includes managing time efficiently and attributing results to effort or strategy use rather than fixed ability (Zimmerman, 2002).

SRL is particularly relevant in online education, where students are expected to manage their studies with greater independence compared to traditional face-to-face settings. Without the structure of a physical campus or regular instructor contact, students must rely more heavily on their own capacity to initiate, sustain, and evaluate their learning—making all three phases of Zimmerman’s model especially significant in this context.

A systematic review by Broadbent and Poon (2015) found that time management, metacognitive monitoring, effort regulation, and critical thinking were positively associated with academic achievement in online higher education. However, these effects were weaker than those found in traditional face-to-face settings, suggesting that additional or context-specific factors may be particularly important for online learners.

2.4 SRL and academic persistence in online education

The ability to access help independently is crucial in online education, where immediate access to instructors is limited compared to campus-based learning. Previous research indicates that online students make extensive use of a variety of resources to support their learning (Fossland & Tømte, 2019; Korstange et al., 2020).

Recent research shows that AI tools have become widely integrated into students’ learning practices. In Norway, data from Studiebarometeret show that 72 percent of students use generative AI to explain topics and course content, while half use it for text quality assurance, summarizing literature, and as a discussion partner (Bjaaland et al., 2025). These tools are valued for their accessibility and immediacy in providing academic support (Aydemir & Seferoğlu, 2024; Lien et al., 2025). However, their usefulness ultimately depends on how students integrate them into their own learning process—meaning that AI use is closely tied to self-regulatory skills and learner autonomy.

Self-regulated learning is a key predictor of both student satisfaction and persistence in online education (Lysitsa & Mavroeidis, 2024). A comprehensive review of persistence and dropout factors in online higher education by Shaikh and Asif (2022) identifies self-regulation, motivation, and time management as the most influential learner-related enablers of persistence. Notably, they also find that younger learners may lack the skills and readiness required for online courses—a challenge particularly relevant for the age group examined in the present study. Students with strong SRL abilities are more likely to complete their courses, even in the absence of frequent instructor guidance (Muljana & Luo, 2019; Shaikh & Asif, 2022). Consequently, understanding how young adults apply or develop self-regulatory strategies during their first year of online higher education is crucial for improving educational practices and supporting student persistence (Broadbent & Poon, 2015).

4.1 Motivations for choosing online studies

The following presentation offers thematically analyzed findings from interviews with five students: Emily, Michael, Nadia, Olivia, and Jacob. The findings revealed four major motivational themes that influenced the participants’ decisions to start higher education through online studies: (1) Flexibility and Autonomy; (2) Combining Studies with Other Commitments; (3) Online Studies as a Transitional Strategy; and (4) Learning, Growth, and Future Prospects.

4.2 Self-regulated learning strategies and academic persistence

The findings revealed how participants employed various self-regulated learning strategies that contributed to their academic persistence. The findings are organized around key components of SRL: (1) planning and goal setting, (2) strategic action and learning behaviors, (3) self-monitoring and adaptive responses, and (4) motivational regulation. Across all four components, the use of AI tools—particularly ChatGPT—emerged as a recurrent support mechanism that participants integrated into their self-regulatory practices. Rather than constituting a separate strategy, AI use appeared to function as a flexible resource that reinforced and extended each of the four components described below.

5.1 Motivations for choosing online studies

The findings suggest a notable convergence with Stephani et al.’s classification of motivations for online learning, particularly within the domains of flexibility and learning (2023, p. 5). Similar to Stephani et al., the participants valued flexibility and autonomy in their decision to pursue online studies. The opportunity to combine education with work and other commitments was a key factor in choosing online education for the majority (4 out of 5) of our participants—a pattern also documented by Schweighart et al. (2024), who found that managing the integration of study demands into existing life structures was one of the most common challenges for new online students.

Stephani et al. further illustrate the importance of flexibility through participants’ reflections on overall time savings and continuous access to learning resources, concluding that ‘a learning program that could be done anytime and anywhere was a good option’ (2023, p. 5). Our findings aligned with this: participants valued fitting study around work, accessing all course materials remotely, and adjusting study hours to suit their own rhythm; as Nadia noted, ‘If midnight works best, then that’s when I work.’

Learning emerged as a primary motivator alongside flexibility. Our interviewees viewed online programs both as spaces for expanding knowledge and competence and as practical means to secure credentials that could advance their careers. The findings therefore indicate that both intrinsic and extrinsic motives operate concurrently. Stephani et al. similarly found that participants were driven both by the goal of obtaining a degree and by a desire to develop personally relevant knowledge and abilities (Stephani et al., 2023). Importantly, this dual motivation—combining instrumental goals with genuine learning interest—is also identified by Shaikh and Asif (2022) as one of the most significant learner-related enablers of persistence in online higher education, offering a bridge between motivational patterns and the SRL strategies discussed in section 5.2.

Contrary to Stephani et al.’s (2023) findings, which emphasize interaction and networking across geographical boundaries as key motivations for choosing online education, this was not a factor identified in our data. None of our participants cited social or peer interaction as a reason for selecting online studies. This is consistent with Brown et al. (2015), who found that around three quarters of first-semester distance learners adopted a “lone wolf” approach to their studies—avoiding contact with teachers and peers and relying on independent problem-solving. All our interviewees were aware that online learning entailed a high degree of independent work; as Emily noted, ‘If you’re […] motivated to work a lot independently, then I would recommend online studies.’ Although peer interaction was not a key motivation for choosing online studies initially, most participants expressed satisfaction with the sporadic peer interaction they did experience, and two highlighted the value of being part of a learning environment with older, more experienced adults. Previous research (e.g., Korstange et al., 2020) has noted that online students often experience a lack of social interaction. Several of our participants echoed this sentiment, stating that they would have preferred more opportunities to interact with both teachers and peers.

Stephani et al. (2023) also identify the online format as a psychologically safe space for expression, especially for introverted students. Among our participants, however, this assumption was not supported as a motivation for choosing online study. All participants indicated that, if circumstances permitted, they would have preferred campus-based education with face-to-face learning and social engagement. This may suggest that, for some young adults transitioning directly from secondary education, the traditional classroom is still perceived as the ideal environment—academically, socially, and emotionally. However, our sample may not be representative of a broader population. Those who agree to participate in a digital research interview may be more extroverted or comfortable with self-disclosure, and the findings should therefore be interpreted with caution.

These divergences may also reflect age-related or developmental differences. While much prior research has focused on adult learners balancing education with work and family, our participants—all first-time students aged 19–21—were in a different life phase, where identity formation, peer belonging, and structured support remained central (Korstange et al., 2020; Schweighart et al., 2024). As Schweighart et al. (2024) specifically note, younger students without prior life experience and self-directedness represent a largely understudied population whose needs and motivations may differ substantially from those of the adult learners that dominate much of the existing literature.

The following section examines how students’ self-regulated learning strategies supported their academic persistence, considering both individual competencies and the role of contextual tools—including artificial intelligence—in an online learning environment.

5.2 Self-regulated learning strategies and academic persistence

In response to the question, ‘How do their self-regulated learning strategies influence their academic persistence?’, our findings indicate that Zimmerman’s framework provides a useful lens for interpreting students’ reflections on their study habits. We find several connections between how our participants described their study practices and the components Zimmerman identifies across the three phases of SRL: forethought, performance, and self-reflection (2002).

A central finding is that all participants emphasized the importance of planning and goal setting, which corresponds with Zimmerman’s forethought phase. Their planning appears primarily aimed at managing the demands of studying alongside other obligations and fulfilling mandatory study requirements. Some students also articulated clear academic goals, such as gaining additional admission points (Nadia and Michael) or completing a bachelor’s degree (Jacob). Notably, all participants had completed and passed their first-semester courses and chosen to continue studying. This may indicate successful application of planning and goal strategies—an observation supported by prior research showing that students with strong SRL skills are more likely to complete online courses (Muljana & Luo, 2019; Shaikh & Asif, 2022). Broadbent and Poon (2015) further found that time management and effort regulation are among the SRL strategies most strongly associated with academic achievement in online higher education, and these were indeed the strategies our participants described most readily and in the most detail.

However, fewer students explicitly described practices associated with Zimmerman’s self-reflection phase. While they reported adjusting their study routines to fit their personal circumstances, they spoke less about adapting their learning processes in response to perceived learning outcomes or instructor feedback. This is notable given that Shaikh and Asif (2022) specifically identify younger learners as being at greater risk of lacking the self-regulatory readiness required for online study—a concern that appears partially relevant here, as several participants seemed to regulate their schedules more than their actual learning strategies. Emily was an exception, actively using short quizzes as a tool to assess her understanding. The participants’ use of discussion forums may also serve as a form of self-monitoring, as they reported using these forums to read questions from peers facing similar challenges. This suggests that forums function not only as platforms for information-seeking, but also as a way for students to reflect on their own understanding by comparing themselves to peers—a form of indirect self-monitoring through observing others’ challenges and questions. These findings underscore the value of integrating opportunities for self-monitoring into course design—through quizzes, open forums, and formative feedback.

In line with the findings of previous studies, our participants also used various tools to support their learning efforts; among these, artificial intelligence has become increasingly important in recent years (Aydemir & Seferoğlu, 2024; Bjaaland et al., 2025; Lien et al., 2025). Several participants expressed that they use AI as a sparring partner in their learning process. As Olivia explained, ‘In physical classrooms, you’re guided through cases and exercises, but online you’re not—so AI replaces the teacher’s role in that regard.’ These findings align with Aydemir and Seferoğlu (2024) and Lien et al. (2025), who show that students value AI tools for their accessibility and immediacy, and with Bjaaland et al. (2025), who found that Norwegian students widely use generative AI for explaining content, summarizing literature, and as a dialogue partner. For online students in particular, this suggests that AI functions not merely as a convenience, but as a compensatory resource in the absence of regular instructor guidance.