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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
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An Exploration of Online Learning Habits and Academic
Productivity of BSIT Students
Genesis G. Camarista, Jan Mark S. Garcia, Edlin Z. Muzones, Jerson T. Valiao, Suzenette M. Javellana,
Michelle D. Deasis, Mark Ian S. Tilog
West Visayas State University-Himamaylan City Campus, Brgy. Caradio-an, Himamaylan City, 6108,
Philippines
DOI: https://doi.org/10.51244/IJRSI.2025.120800320
Received: 26 Sep 2025; Accepted: 02 Oct 2025; Published: 11 October 2025
ABSTRACT
This study explores the relationship between online learning habits and academic productivity among Bachelor
of Science in Information Technology (BSIT) students at West Visayas State University–Himamaylan City
Campus (WVSU-HCC) during the first semester of Academic Year 2025–2026. Grounded in Zimmerman’s
(1989) Self-Regulated Learning (SRL) theory, Davis’s (1989) Technology Acceptance Model (TAM),
Vygotsky’s (1978) Constructivist Learning Theory, Sweller’s (1988) Cognitive Load Theory, and Astin’s
(1984) Theory of Student Involvement, the study investigates how students’ learning behaviors in digital
environments impact their academic productivity. A quantitative-correlational research design was employed,
involving 117 BSIT students selected through stratified random sampling. Data were collected using a
validated and reliable researcher-developed survey instrument, with internal consistency coefficients of α =
.916 for online learning habits and α = .910 for academic productivity. Descriptive statistics and Spearman’s
rank-order correlation were used to analyze the data. Results revealed that BSIT students commonly engaged
in productive online learning habits, including collaboration, time management, and digital tool use. A
statistically significant and strong positive correlation was found between students’ online learning habits and
their academic productivity. These findings suggest that well-developed self-regulatory and digital
competencies are predictive of higher academic output in online and blended learning environments. The study
concludes with recommendations for integrating SRL training, promoting peer collaboration, optimizing
instructional design, and implementing institutional strategies that support cognitive and emotional
engagement in virtual learning settings.
Keywords: online learning habits, academic productivity, self-regulated learning, digital tools, higher
education, BSIT students, blended learning, technology acceptance model
INTRODUCTION
Background of the study
The shift to online and blended learning—accelerated by the COVID-19 pandemic—has transformed higher
education globally. Online learning demands greater autonomy, self-regulation, and effective learning habits
from students. Numerous studies have examined how students’ online learning habits influence their academic
outcomes. For example, Broadbent & Poon (2015) found that Australian university students who engaged in
self-regulated learning strategies (time management, goal setting, self-monitoring) performed significantly
better in online courses. Similarly, Kebritchi, Lipschuetz, & Santiague (2017) summarized international
literature demonstrating that students who organized consistent study schedules and used appropriate digital
tools had higher academic satisfaction and grades.
At the national level, in the Philippines, several studies have begun exploring online learning among higher
education students. For one, Talidong & Paringit (2021) studied technology use among Philippine college
students, concluding that while many are proficient technically, inconsistent study environments and
distractions undermine learning outcomes. However, specific studies focusing on BSIT students—who often
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have heavier digital workloads, complex technical requirements, and sometimes unstable internet
connections—are still scarce.
Locally, in the Visayas region, literature is still limited. Some reports and institutional surveys suggest that
BSIT students in local universities struggle with online learning due to intermittent internet, inadequate access
to devices, and lack of stable learning environments at home. But these reports are often anecdotal or
unpublished, lacking rigorous survey-based quantitative data. There is a dearth of published work that
measures both online learning habits (e.g., scheduling, platform usage, self-regulation) and academic
productivity (e.g., completion of tasks, quality of outputs, grades) among BSIT students in this locale.
A review of existing literature reveals several key gaps that this study seeks to address. First, there exists a
specific population gap, as most prior studies tend to focus on general college or university populations, with
limited attention given specifically to Bachelor of Science in Information Technology (BSIT) students. This is
a critical oversight, given that BSIT students often engage in coursework that includes complex computational
tasks, online laboratories, and programming exercises—learning activities that pose unique cognitive and
technical demands not always present in other disciplines.
Second, a combined variables gap is evident. While numerous studies investigate either online learning habits
or academic productivity independently, few examine the relationship between these two variables
simultaneously. This limits our understanding of how students’ behavioral patterns in online learning contexts
influence tangible academic outcomes.
Third, there is a geographical and local data gap, particularly in the Philippine context and more specifically in
regions such as Western Visayas and Iloilo. Localized empirical data on the online learning practices and
academic productivity of BSIT students are sparse, making it difficult to design regionally responsive
educational interventions or draw broader national generalizations.
Fourth, the literature reveals a contextual and environmental variables gap. Few existing studies investigate
how moderating factors such as internet reliability, access to personal learning devices, and home study
environments shape or influence the relationship between learning habits and academic success. These
variables are particularly pertinent in low- and middle-income contexts where infrastructural inequities persist.
Finally, a directional and methodological gap is also apparent. Many studies utilize purely descriptive or
qualitative approaches, with fewer employing quantitative, correlational designs that can provide insight into
the strength and direction of relationships between variables. This restricts the theoretical advancement of our
understanding of causality or predictive associations in this domain.
This research responds to these identified gaps in several ways. First, by focusing specifically on BSIT
students, the study accounts for the technical and digital nuances of their academic workload, offering insights
that are more aligned with the cognitive and practical demands of computing education. Second, it
simultaneously measures both online learning habits and academic productivity using validated survey
instruments. A more comprehensive understanding of the relationship between behavioral learning strategies
and academic outcomes is made possible by this dual approach.
Third, by focusing on a particular local setting (such as a university in Western Visayas), the study offers
insightful empirical data that can guide regional and national institutional decision-making and policy
development. This is especially important when developing student support systems and focused interventions
to address local needs. Fourth, the study incorporates a number of contextual factors, including the physical
learning space, internet stability, and device access. More detailed analyses are made possible by the inclusion
of these moderators, which aid in identifying mediating factors and interaction effects that may have an impact
on the association between productivity and online learning practices. Lastly, the study's methodological rigor
is strengthened by the application of a quantitative correlational design. This method advances theoretical
knowledge and practical consequences in educational technology research by enabling the evaluation of the
strength and direction of relationships between academic output and learning habits. The study intends to fill
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these various gaps in order to provide a significant and contextually based body of knowledge that connects
the theoretical, empirical, and practical areas of online learning research.
In light of these limitations, the purpose of this study is to investigate how the academic output of BSIT
students at West Visayas State University-Himamaylan City Campus (WVSU-HCC), a satellite campus
located in Negros Occidental, the Philippines, is related to their online studying habits. Its specific objectives
are to ascertain the habits of online learning, evaluate academic productivity, and ascertain the connection
between the two.
Theoretical Framework
The present study investigates the connection between academic productivity and online learning behaviors
among students pursuing a Bachelor of Science in Information Technology (BSIT). Understanding the
mechanics behind students' learning behaviors and outcomes in a digital learning environment is made easier
by grounding this inquiry in pertinent educational and psychological theories.
Zimmerman's (1989) theory of Self-Regulated Learning (SRL), which holds that learners' capacity to control
their cognitive, metacognitive, behavioral, and motivational processes has a major impact on academic
achievement, lies at the heart of this investigation. Effective online learning requires goal-setting, time
management, self-monitoring, and strategic help-seeking, all of which are incorporated into SRL (Panadero et
al. 2017). This study looks at observable behaviors that are consistent with SRL characteristics, such as
planning study time, avoiding distractions, and actively interacting with course materials.
Students are frequently expected to assume more responsibility for their education in online learning
environments. This is consistent with constructivist learning theory, especially the ideas of Piaget (1952) and
Vygotsky (1978), who highlighted how social contact and active involvement help students build knowledge.
The constructivist theory, which holds that knowledge is created rather than passively acquired, is the
foundation of BSIT students' online learning practices, which include using interactive tools, working on
digital projects, and taking part in discussion forums.
Another useful tool for describing how students embrace and use technology in online learning environments
is Davis's (1989) Technology Acceptance Model (TAM). According to TAM, people' acceptance of
technology is influenced by its perceived utility and usability. Students' opinions regarding these technologies
may have an impact on their regular use of digital communication tools, educational software, and online
learning platforms, which in turn may affect academic output.
In order to comprehend how students' online learning habits control the mental effort involved in information
processing, the study also makes use of Cognitive Load Theory (Sweller, 1988). While good habits (such as
summarizing texts, strategically using multimedia, and taking frequent breaks) can optimize intrinsic and
germane load and improve academic achievements, poorly structured online study behaviors can result in
extraneous cognitive load (Paas & Van Merriënboer, 1994).
Last but not least, Astin's (1984) Theory of Student Involvement informs the idea of academic production by
arguing that both the amount and quality of a student's involvement in academic activities determines their
learning. In this study, productivity is not solely determined by grades; it also includes involvement in
coursework, task completion, time management, and use of digital learning resources.
Through the integration of different theoretical viewpoints, this study investigates the ways in which students'
acceptance and efficient use of technology promote self-regulated and constructivist learning behaviors, which
in turn enhance academic output. In the context of BSIT students' online learning, the theoretical framework
provides a multifaceted lens through which to examine the intricate relationship between academic
performance and behavioral patterns.
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METHODOLOGY
Research Design
A quantitative-correlational research approach was used in this study to investigate the connection between
BSIT students' online learning practices and academic production. Finding out if there is a statistically
significant correlation between students' self-reported academic productivity in online or mixed learning
environments and their online learning activities was the main goal of this study.
A correlational research approach is appropriate for this investigation because it allows the researcher to
analyze naturally occurring variations in the variables without manipulating any of the conditions (Creswell &
Creswell, 2018). It enables the assessment of the strength and direction of the relationship between online
learning habits—such as time management, digital tool usage, collaboration, and help-seeking—and levels of
academic productivity, operationalized through self-assessment tools.
Furthermore, this design not only supports the exploration of associations between variables but also facilitates
the identification of potential predictors of academic productivity in technology-mediated learning
environments. It aligns with the study’s objective of providing empirical evidence to inform instructional
design, student support mechanisms, and educational policy regarding online learning readiness and
effectiveness.
Descriptive statistics is used to profile the respondents' learning behaviors and productivity levels, while
Pearson’s correlation coefficient is used to determine the strength and direction of the relationship between the
two primary variables. The use of standardized self-report instruments ensure consistency in data collection
and measurement. Data were collected using validated survey instruments and analyzed using SPSS software.
This design allows for a broad understanding of behavioral patterns among BSIT students and offer insights
into how self-regulated learning practices, in the context of online education, influence academic productivity.
Participants and Inclusion Criteria
The participants of this study consist of 117 BSIT students officially enrolled at a state university during the
first semester of the academic year 2025–2026. These students were selected to represent a cross-section of the
BSIT population across all academic year levels, ensuring diversity in academic exposure and experience.
Participation in the study was voluntary, and all students were informed about the purpose, procedures, and
ethical considerations prior to data collection.
To be eligible, students must meet the following inclusion criteria: (1) they must be officially enrolled in the
BSIT program; (2) they must be actively attending classes during the first semester of A.Y. 2025–2026; and
(3) they must voluntarily consent to participate in the study. These criteria ensure that only current, actively
engaged students are included, enhancing the relevance and validity of the data collected regarding online
learning habits and academic productivity.
Sampling Technique and Sample Size
The study employed a stratified random sampling technique to select a sample of 117 participants. This
sampling method involves dividing the total population of BSIT students into strata based on academic year
level—first year, second year, third year, and fourth year. From each stratum, participants were randomly
selected in proportion to their representation in the overall population. This approach is intended to ensure that
all year levels are fairly represented, thereby increasing the generalizability and accuracy of the findings within
the context of the BSIT program.
The sample size of 117 was determined to be adequate based on statistical considerations for correlational
research, ensuring sufficient statistical power to detect meaningful relationships between variables such as
online learning habits and academic productivity.
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Data Collection Instrument
The primary instrument used in this study was a researcher-designed structured survey questionnaire composed
of three major parts, each aimed at capturing specific aspects of the participants’ profile, online learning habits,
and academic productivity in digital learning environments.
Part I: Demographic Profile. This section collected essential background information about the participants,
including age, gender, year level, and frequency of internet access. These variables were intended to provide
context and enable subgroup analysis in relation to online learning habits and productivity.
Part II: Online Learning Habits. This section assessed students’ self-reported online learning behaviors and
digital literacy competencies. Items covered domains such as time management, use of productivity tools (e.g.,
calendars, to-do lists), communication habits, collaboration, help-seeking behavior, and use of educational
technology.
Part III: Academic Productivity in Online or Blended Learning Settings. This portion measured students’
perceived productivity across several dimensions, including task completion, academic confidence, time
management, and performance outcomes. Items were developed based on prior research (Panadero et al.,
2017) and aligned with cognitive and behavioral indicators of academic output in digital learning
environments.
All survey items in Parts II and III were rated using a 5-point Likert scale allowing the quantification of
behaviors and perceptions for both descriptive and inferential statistical analyses.
Validity and Reliability of the Data
To ensure the rigor and trustworthiness of the measurement tool, the survey instrument underwent both
validity and reliability testing. Face and content validity were established through expert review by faculty
members with specialization in educational technology and research methodology. These experts evaluated the
questionnaire for clarity, relevance, and alignment with the research objectives and theoretical frameworks,
including Zimmerman’s (1989) Self-Regulated Learning (SRL) theory and Davis’s (1989) Technology
Acceptance Model (TAM). Revisions were made based on their feedback to enhance the instrument’s
precision and coherence.
In terms of reliability, internal consistency was evaluated using Cronbach’s alpha during a pilot test involving
a comparable group of BSIT students. The Online Learning Habits section (Part II) yielded a Cronbach’s alpha
of .916, while the Academic Productivity section (Part III) reported an alpha of .910. These coefficients exceed
the commonly accepted threshold of α > .80 (George & Mallery, 2003), indicating high internal consistency
and reliability of the items within each construct. The use of a 5-point Likert scale across both sections
facilitated the quantification of participant responses, making the data amenable to both descriptive and
inferential statistical analyses. These results affirm that the instrument is both psychometrically sound and
suitable for assessing the constructs under investigation.
Data Analysis Procedure
The data collected in this study were analyzed using both descriptive and inferential statistical techniques.
Descriptive statistics—including means and standard deviations—were be computed to summarize the
participants’ online learning habits and levels of academic productivity. These descriptive measures provided
an overview of how BSIT students from WVSU-HCC typically engage with digital learning environments and
perceive their academic output.
To determine the relationship between online learning habits and academic productivity, the study employed
Spearman’s rank-order correlation coefficient (Spearman’s ρ). This non-parametric test is appropriate given
the ordinal nature of the Likert scale data and the possibility of non-normal distribution. Spearman’s ρ will
assess the strength and direction of the monotonic relationship between the two primary variables: online
learning habits and academic productivity.
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All statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS), version
26. The significance level was set at α = .05, meaning that results with a p-value less than .05 was considered
statistically significant. The internal consistency of the instrument was previously established using
Cronbach’s alpha, with reliability coefficients exceeding the acceptable threshold (α > .80), supporting the
validity of subsequent inferences drawn from the data.
This combination of descriptive and correlational techniques enabled the researcher to identify patterns in
learning behavior and test the hypothesized relationship between students’ online learning habits and their self-
reported academic productivity.
Ethical Considerations
The study strictly adhered to ethical research standards to protect the rights and welfare of the participants. The
following principles were observed:
Informed Consent: All participants were provided with a consent form detailing the purpose of the study,
voluntary participation, anonymity, and the right to withdraw at any time without consequence.
Confidentiality: No personally identifiable information was collected. Data were handled confidentially and
used only for research purposes.
Data Protection: All data were securely stored and only accessible to the researcher.
Approval: Prior to data collection, the study received approval from the WVSU-HCC Research Ethics
Committee.
These measures ensured that the research process was ethical, responsible, and respectful of participants’ rights
and dignity, in accordance with the principles outlined by the National Ethical Guidelines for Health and
Health-Related Research (Philippines, 2022).
RESULTS AND DISCUSSIONS
Table 1. Common Online Learning Habits of BSIT Students
Online Habit
Mean
SD
Interpretation
Rank
In terms of study routine and environment
1. I take regular breaks to maintain focus while studying
online.
3.90
.98
Common
1
2. I study in a quiet, distraction-free environment during
online classes.
3.85
.97
Common
2
3. I prepare all necessary materials before starting an online
learning session.
3.80
.98
Common
3
4. I create a specific schedule for my online study sessions.
3.56
1.00
Common
4
In terms of time management and organization
1. I allocate sufficient time daily/weekly for online learning
activities.
3.85
.82
Common
1
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2. I set specific goals for what I want to accomplish in each
online learning
session.
3.79
.89
Common
2
3. I use digital tools (e.g., calendars, reminders) to manage
my online
learning tasks.
3.66
1.03
Common
3
4. I avoid procrastinating on assignments and deadlines for
online courses.
3.58
1.03
Common
4
In terms of engagement and participation
1. I collaborate with classmates on group projects or study
sessions online.
3.96
.87
Common
1
2. I regularly review and reflect on feedback provided by
instructors online.
3.81
.88
Common
2
3. I actively participate in online discussions and forums
related to my
courses.
3.65
1.01
Common
3
4. I ask questions or seek clarification during live online
classes or via
email/chat.
3.60
1.02
Common
4
In terms of resource utilization
1. I seek help from instructors or peers promptly when I
encounter
difficulties.
3.90
.90
Common
1
2. I make use of supplementary online resources (videos,
tutorials, articles) to aid my learning.
3.85
.92
Common
2
3. I regularly access the learning management system (LMS)
to check for
updates and materials.
3.70
.89
Common
3
4. I use note-taking apps or tools during online lectures.
3.64
.98
Common
4
Self-regulation and motivation
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1. I monitor my own progress and adjust my study habits as
needed.
3.80
.88
Common
1
2. I am self-motivated to complete my online coursework
even without direct
supervision.
3.67
.89
Common
2
3. I manage my time and responsibilities related to online
learning.
3.66
.97
Common
3
4. I stay focused during online learning sessions.
3.46
1.05
Moderately
Common
4
Note: 4.51-5.00 Very common; 3.51-4.50 Common; 2.51-3.50 Moderately Common; 1.51-2.50 Rare; 1.00-
1.50 Very rare
The findings presented in Table 1 reveal a well-rounded profile of the online learning habits of BSIT students,
with all items across five domains interpreted as “common” or “moderately common,” based on their mean
scores. These results highlight a generally positive trend in students’ online learning behaviors, particularly in
areas such as collaboration, study routines, resource utilization, and time management. These habits align with
key elements of Zimmerman’s (1989) Self-Regulated Learning (SRL) theory, where learners demonstrate
forethought, performance, and self-reflection in their academic behaviors. The ability to set study schedules,
allocate time, monitor progress, and seek help promptly reflects self-regulated behavior essential for success in
online environments.
Zimmerman’s SRL model posits that students who engage in goal-setting, strategic planning, and self-
monitoring are more likely to succeed. The high-ranking item “I collaborate with classmates on group projects
or study sessions online” (M=3.96) suggests that BSIT students are not just individually driven but also
socially oriented learners. This aligns with Constructivist Learning Theory, which emphasizes the role of
social interaction and contextual learning. According to Vygotsky (1978), learning is inherently social and
occurs through active participation with peers and more knowledgeable others. The collaborative practices
observed here reflect an active engagement in constructing knowledge, not merely absorbing information.
In the domain of resource utilization, the item “I seek help from instructors or peers promptly when I
encounter difficulties” (M=3.90) supports the idea that BSIT students are proactive in overcoming learning
barriers—again reinforcing self-regulation and also suggesting a high degree of student involvement, as
articulated in Astin’s (1984) Theory of Student Involvement. Astin posited that student learning and
development are directly proportional to the quality and quantity of student involvement in both academic and
co-curricular activities. Prompt help-seeking and frequent use of supplementary resources (M=3.85) reflect
strong cognitive and emotional investment in learning.
In terms of study routine and environment, among the specific habits reported, the highest-ranked was "taking
regular breaks to maintain focus" (M = 3.90, SD = 0.98), followed closely by studying in a distraction-free
environment (M = 3.85, SD = 0.97). These results reflect several theoretical underpinnings. From the
perspective of Zimmerman’s (1989) Self-Regulated Learning (SRL) theory, the behaviors identified—such as
preparation of materials, maintaining a focused environment, and taking breaks—are indicative of strategic
self-regulation where learners exercise metacognitive control over their study environments. However, the
relatively lower mean for scheduling study sessions suggests a partial gap in the forethought phase of SRL,
particularly in time planning, which Zimmerman emphasized as a key element of effective self-regulated
learning. Comparatively, these findings are consistent with those of Broadbent and Poon (2015), who
highlighted the role of time management and environment structuring as essential predictors of academic
success in online learning settings. Similarly, Panadero et al. (2017) supported the notion that well-regulated
learners tend to engage in structured, deliberate routines. However, contrasting findings from Cho and Shen
(2013) suggest that without institutional scaffolding, students often struggle to translate awareness of
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productive habits into consistent practice—particularly in managing study schedules, which aligns with the
current study’s lowest mean score in this domain.
Furthermore, the domain of self-regulation and motivation had slightly lower mean scores, especially “I stay
focused during online learning sessions” (M=3.46), categorized as “moderately common.” This signals
potential cognitive challenges related to sustained attention in digital environments. Sweller’s (1988)
Cognitive Load Theory can help explain this finding. The online setting, with its myriad stimuli and
multitasking demands, may increase extraneous cognitive load, thereby reducing students’ ability to maintain
focus. This emphasizes the need for instructional design that minimizes distractions and enhances germane
cognitive load, thereby facilitating deeper learning.
In terms of technology use, the statement “I use digital tools (e.g., calendars, reminders) to manage my online
learning tasks” (M=3.66) reflects a healthy level of technology acceptance, which is consistent with Davis’
(1989) Technology Acceptance Model (TAM). According to TAM, perceived ease of use and perceived
usefulness influence an individual’s intention to use technology. The frequent use of LMS and digital
organization tools suggests that BSIT students find these platforms both manageable and beneficial to their
learning tasks.
These findings align with several related studies. For instance, Broadbent and Poon (2015) found that self-
regulated learning strategies such as time management, goal setting, and help-seeking significantly predicted
academic success in online learners. Similarly, Panadero et al. (2017) emphasized that learners who practice
metacognitive regulation and maintain structured study environments perform better in virtual settings.
Conversely, the moderately common rating for maintaining focus contradicts Kahu and Nelson’s (2018)
assertion that engagement is holistic and often sustained by the emotional and behavioral connection to course
content. The slightly lower focus may also point to Zoom fatigue or screen-related burnout as documented in
Bailenson (2021).
In conclusion, BSIT students demonstrate commendable levels of self-regulation, motivation, collaboration,
and technology integration in their online learning practices. These behaviors are theoretically grounded in
established learning theories—most notably SRL, Constructivism, TAM, Cognitive Load Theory, and Student
Involvement Theory. However, the moderate scores on sustained focus raise important implications for
educators and curriculum designers to consider strategies that reduce cognitive overload, increase interactivity,
and foster deeper emotional engagement in online and blended learning environments.
Table 2. BSIT Students Assessment of their own Productivity in Online or Blended Settings
Components
n
Mean
SD
Interpretation
Task Completion and
Academic Output
117
3.77
.75
High Productivity
Time Management and Study
Habits
117
3.81
.78
High Productivity
Self-Efficacy and Academic
Confidence
117
3.74
.83
High Productivity
Academic Outcomes and
Performance
117
3.64
.84
High Productivity
Overall Productivity
117
3.74
.75
High Productivity
Note: 4.51-5.00 Very High Productivity; 3.51-4.50 High Productivity; 2.51-3.50 Moderate Productivity; 1.51-
2.50 Low; 1.00-1.50 Very Low
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The results presented in Table 2 provide a comprehensive view of how BSIT students assess their own
productivity in online or blended learning settings. Across all four measured components—Task Completion
and Academic Output (M=3.77), Time Management and Study Habits (M=3.81), Self-Efficacy and Academic
Confidence (M=3.74), and Academic Outcomes and Performance (M=3.64)—students reported high levels of
productivity, with an overall mean score of 3.74. These findings indicate that BSIT students perceive
themselves as effectively managing and performing within online or blended modalities, a result that aligns
with several key theoretical frameworks in educational psychology and technology-enhanced learning.
Foremost among these is Zimmerman's (1989) theory of Self-Regulated Learning (SRL), which posits that
learners who actively engage in metacognitive processes such as goal setting, self-monitoring, and strategic
action are more likely to experience academic success. The high scores in Time Management and Study Habits
and Task Completion directly reflect these SRL components, suggesting that BSIT students have developed
effective strategies to navigate the less-structured nature of online environments. The reported high self-
efficacy and academic confidence further reinforce Zimmerman’s assertion that belief in one’s own abilities is
central to sustained motivation and performance.
Additionally, the findings resonate strongly with Constructivist Learning Theory, which emphasizes active,
learner-centered engagement and the importance of context and personal meaning-making. In a blended or
online setup, students must construct their own learning pathways, often without the constant physical
presence of instructors. The high productivity scores suggest that BSIT students are not passive recipients of
content but are engaging with materials, peers, and digital platforms in ways that foster deeper understanding.
This supports Vygotsky’s notion of learners as co-constructors of knowledge, even in digitally mediated
environments.
From a technological standpoint, these findings are congruent with the Technology Acceptance Model (TAM)
proposed by Davis (1989). TAM highlights perceived usefulness and perceived ease of use as central
determinants of technology adoption and utilization. Students who report high productivity in blended learning
environments are likely those who find the technological tools both efficient and user-friendly. Their ability to
manage time, complete tasks, and feel academically confident suggests that digital platforms, such as learning
management systems (LMS), video conferencing tools, and digital note-taking apps, are well integrated into
their learning routines. This is in line with findings by Teo (2011), who confirmed that perceived ease of use
significantly predicts academic performance in tech-enhanced learning contexts.
Despite this generally positive trend, the relatively lower mean for Academic Outcomes and Performance
(M=3.64), although still within the “High” range, may hint at the cognitive challenges associated with online
learning. According to Sweller’s (1988) Cognitive Load Theory, excessive extraneous cognitive load—often
triggered by multitasking, poorly designed interfaces, or insufficient instructional support—can hinder learning
performance. While students report managing their time and habits well, the slightly lower score in academic
outcomes could be symptomatic of the hidden mental effort required to process information in digital settings.
This finding is supported by recent studies (e.g., Paas & Sweller, 2014), which emphasize the need for
instructional design that reduces unnecessary cognitive demands.
Moreover, the results closely align with Astin’s (1984) Theory of Student Involvement, which asserts that
student learning is directly proportional to the amount of physical and psychological energy devoted to the
academic experience. The high levels of self-assessed productivity suggest that BSIT students are deeply
involved in their learning processes, even in the absence of traditional classroom structures. Their engagement
in time management, academic tasks, and self-evaluation reflects high levels of both behavioral and emotional
investment. Similar findings were reported by Fredricks, Blumenfeld, and Paris (2004), who noted that self-
perceived involvement is a strong predictor of academic success in both face-to-face and online learning
environments.
Corroborating these findings, Broadbent and Poon (2015) in their meta-analysis emphasized that students with
high levels of self-regulation and time management consistently outperform their peers in online learning
environments. Likewise, Panadero et al. (2017) underscored that academic confidence and self-efficacy are
critical drivers of student success in technology-mediated learning. On the other hand, Kahu and Nelson
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(2018) argued that cognitive and emotional engagement must be supported institutionally; thus, while
individual productivity is commendable, institutional efforts in instructional design and student support remain
critical to sustaining these high productivity levels.
The study's results provide compelling evidence that BSIT students are well-adapted to online and blended
learning environments, as reflected in their high self-assessed productivity across multiple academic
dimensions. The integration of self-regulated behaviors, effective technology use, and student involvement
supports a holistic picture of competent and adaptive learners. However, the slightly lower score in academic
outcomes underscores the importance of addressing cognitive load and instructional quality in digital
environments. Moving forward, these insights can inform instructional strategies, curriculum design, and
policy decisions aimed at further enhancing productivity and academic success in blended and online higher
education contexts.
Table 3. Relationship Between Online Learning Habits and the Academic Productivity of BSIT Students
Habits
Productivi
ty
Habits
Correlation
Coefficient
1.000
.853
**
Sig. (2-tailed)
.
.000
Productivi
ty
Correlation
Coefficient
.853
**
1.000
Sig. (2-tailed)
.000
.
Note: **p < .01 – Significant at .01 level
Pearson’s r: ±0.1 to ±0.3 – Small/Weak, ±0.3 to ±0.5 – Medium/Moderate,
±0.5 to ±1.0 – Large/Strong
The findings of the present study reveal a statistically significant and strong positive correlation between
online learning habits and academic productivity among BSIT students, as evidenced by a Pearson correlation
coefficient of 0.853 (p < .01). This result clearly indicates that students who exhibit more consistent and
effective online learning habits—such as structured study routines, effective time management, goal-setting,
engagement with digital tools, and proactive use of learning resources—tend to report higher levels of
academic productivity in online or blended learning environments. Given that a correlation coefficient within
the range of ±0.5 to ±1.0 is considered strong, the observed r-value of 0.853 suggests a robust and meaningful
association between the two variables.
This finding aligns closely with Zimmerman's (1989) Theory of Self-Regulated Learning (SRL), which
underscores the importance of learners actively managing their learning through goal-setting, self-monitoring,
and strategic action. Online learning environments require a high level of autonomy, and students who possess
strong self-regulatory habits are more capable of sustaining academic engagement and achieving desired
outcomes (Panadero et al., 2017). These habits are also consistent with Astin’s (1984) Theory of Student
Involvement, which posits that the amount of physical and psychological energy students invest in their
academic experience is directly related to learning and development. In digital settings, involvement often
manifests as routine engagement with learning platforms, peer collaboration, and continuous self-assessment—
all components reflected in the study’s habit indicators.
Furthermore, the significant correlation observed in this study is supported by earlier research. For instance,
Broadbent and Poon (2015) conducted a meta-analysis showing that self-regulated learning strategies,
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including time management and effort regulation, were significant predictors of academic achievement in
online contexts. Similarly, Kizilcec et al. (2017) found that learners who set specific learning goals and
monitored their progress were more likely to succeed in Massive Open Online Courses (MOOCs), indicating
that habitual behaviors strongly influence productivity and completion rates. The present study extends these
findings to the context of BSIT students in the Philippines, emphasizing that productive online learning
behaviors are not only theoretically grounded but also empirically validated across diverse educational
settings.
Moreover, the findings can be interpreted through the lens of Davis’s (1989) Technology Acceptance Model
(TAM), which posits that users’ perceptions of the usefulness and ease of use of technology shape their
engagement with digital platforms. Students who have internalized effective online learning habits are likely
those who perceive the digital tools and environments as beneficial, leading to higher levels of motivation and
academic output. This is further supported by Cognitive Load Theory (Sweller, 1988), which argues that
minimizing extraneous cognitive load—achievable through organized study habits—enhances learning
efficiency. BSIT students who prepare study materials in advance, minimize distractions, and follow consistent
routines are effectively managing their cognitive resources, thereby boosting productivity.
While these findings confirm a strong relationship between habits and productivity, they also signal the need
for educational institutions to actively support the development of these behaviors. This echoes the call by
Alqurashi (2019), who emphasized the role of learner satisfaction, motivation, and self-efficacy in shaping
outcomes in online environments. Without institutional scaffolding and explicit instruction in habit formation,
some students may struggle to self-regulate effectively—particularly those transitioning from traditional to
online or blended modalities.
The statistically significant and strong correlation between online learning habits and academic productivity
affirms the critical role of student behaviors in online education. Grounded in multiple theoretical frameworks
and corroborated by global literature, the study reinforces that the cultivation of effective learning habits is not
ancillary but essential to academic success in digitally mediated learning environments. Future research should
investigate causality and explore intervention strategies that can further strengthen students' self-regulatory
capacities, especially within technology-driven degree programs such as BSIT.
CONCLUSIONS
The statistically significant and strong positive correlation found between online learning habits and academic
productivity among BSIT students can perhaps be attributed to the increasing self-regulatory demands of
digital learning environments. Students who exhibit strong habits—such as structured time management,
effective use of digital tools, and proactive help-seeking—are probably those who have developed a higher
degree of metacognitive control, as posited by Zimmerman’s (1989) Self-Regulated Learning (SRL) theory.
Their behaviors likely reflect the forethought and performance phases of SRL, which enable them to adapt
more successfully to the autonomy required in online education.
Moreover, the emphasis on collaboration and peer interaction suggests that students are not merely navigating
content individually but are engaging in socially mediated learning experiences. This is probably why their
productivity remains high—because they are constructing knowledge in alignment with the principles of
Constructivist Learning Theory and Vygotsky’s (1978) emphasis on learning through interaction. The use of
digital tools also supports this, as students likely perceive these technologies as both useful and manageable, in
line with Davis’ (1989) Technology Acceptance Model (TAM), thereby encouraging their sustained use and
contributing to academic output.
The slightly lower mean score in sustained focus may be explained by the cognitive demands of navigating
online platforms, perhaps pointing to increased extraneous cognitive load, as explained in Sweller’s (1988)
Cognitive Load Theory. While students are generally effective at managing their tasks, they may still struggle
with distractions and fatigue associated with continuous screen exposure—factors that likely diminish
sustained attention despite otherwise productive habits.
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It is also probable that the high levels of reported productivity are a reflection of deep student involvement, as
described by Astin’s (1984) theory. Students who invest time, attention, and emotional energy in their
learning—through strategic study behaviors and engagement with peers and instructors—tend to experience
greater academic gains. The findings may also be indicative of the increasing normalization of online and
blended modalities, where students have gradually adapted their learning strategies to fit the demands of a
digital academic environment.
In summary, the strong relationship between online learning habits and academic productivity is likely the
result of multiple intersecting factors: effective self-regulation, perceived ease of use and utility of technology,
collaborative engagement, and strategic management of cognitive load. These findings reinforce the
importance of designing learning environments that not only deliver content but also scaffold the development
of productive academic behaviors. Future research may explore the causal mechanisms behind these
relationships and test intervention strategies that further enhance student self-regulation and digital
engagement.
RECOMMENDATIONS
To sustain and enhance the academic productivity of students in online and blended learning environments,
several targeted and theoretically grounded interventions are recommended. First, integrating self-regulated
learning (SRL) skills into the curriculum is essential. Embedding modules that focus on time management,
goal-setting, self-monitoring, and reflective practices within foundational BSIT courses can cultivate
metacognitive control and independent learning strategies among students. These modules may be
implemented through short asynchronous video tutorials, digital journals, and embedded reflective
assignments to ensure accessibility and sustained engagement.
Second, providing structured training on digital productivity tools can further reinforce effective learning
habits. In line with Davis’s (1989) Technology Acceptance Model (TAM), students are more likely to engage
with educational technologies when they perceive them as useful and easy to use. Therefore, offering
workshops and tutorials on the use of digital calendars, reminders, note-taking applications, and learning
management systems (LMS) can improve both technology adoption and academic efficiency. A practical
approach would be to initiate a “Digital Toolbox” webinar series at the beginning of each semester,
supplemented by classroom integration of these tools to reinforce their relevance.
Third, structured peer collaboration should be embedded in course design. Rooted in Vygotsky’s (1978)
Constructivist Learning Theory, peer interaction facilitates knowledge co-construction and deepens conceptual
understanding. Designing online tasks that require collaborative efforts—such as group projects, peer reviews,
and coding sessions—can strengthen both academic and social engagement. These can be operationalized
using breakout rooms, shared documents, and asynchronous discussion boards, with clear role assignments and
rotating leadership to promote accountability.
Fourth, to address issues related to mental strain and digital fatigue, course content should be designed to
minimize extraneous cognitive load. According to Sweller’s (1988) Cognitive Load Theory, learners benefit
most when instructional materials reduce unnecessary complexity and allow cognitive resources to focus on
essential learning tasks. This can be achieved by simplifying LMS navigation, eliminating redundant
multimedia elements, and ensuring that learning objectives are clear and concise. Usability testing with
students, coupled with adherence to instructional design best practices such as chunking and consistency, will
enhance learning efficiency.
Fifth, the implementation of "focus boosting" strategies is recommended to address the study’s observed
decline in sustained attention. Techniques such as the Pomodoro method, scheduled digital detox periods, and
guided breaks can help students maintain cognitive stamina. Institutions may support this by distributing
digital “Focus Kits,” integrating focus routines in class sessions, or creating structured “deep work” periods
during synchronous activities.
Additionally, establishing a virtual mentoring and academic coaching program can foster greater student
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involvement and well-being. In line with Astin’s (1984) Theory of Student Involvement, pairing students with
faculty or peer mentors can increase both cognitive and emotional engagement with academic tasks. Monthly
virtual check-ins, goal-setting sessions, and open forums for academic and emotional support can significantly
boost motivation and retention.
An institutionalized online learning readiness orientation may also be required for all students engaged in
online or blended learning. This orientation can assess students' self-regulated learning behaviors, digital
literacy, and motivational readiness. Following these assessments, students should receive tailored feedback
and resources to strengthen their preparedness, thus increasing the likelihood of success in digitally mediated
courses.
To promote long-term metacognitive growth, reflective practices may be embedded throughout the course.
Weekly learning journals, digital exit tickets, or e-portfolio reflections can help students assess their progress
and refine learning strategies. These practices, grounded in both SRL and constructivist frameworks, foster
continuous self-awareness and adaptive learning behavior.
Finally, further research and continuous monitoring of online learning interventions are necessary.
Longitudinal studies assessing the effectiveness of SRL training, digital tool integration, and collaborative
strategies will strengthen the evidence base and inform policy adjustments. In particular, causal research
exploring how specific interventions impact academic performance across different student demographics will
provide nuanced insights into optimizing online education strategies. To complement these efforts, institution-
level strategies, including comprehensive digital literacy training and structured mentoring programs, should
also be prioritized. These strategies would help ensure that students are not only proficient in using digital
tools but also receive consistent, personalized support, which can enhance their academic performance,
engagement, and overall success in online learning environments.
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