Blended Learning Course Experience Scale (BLCES)

Abstract

The Blended Learning Course Experience Scale (BLCES) is a psychometric instrument designed to evaluate undergraduate and postgraduate students’ subjective educational experiences, pedagogical perceptions, and psychological responses within hybrid or blended higher education environments. Developed and psychometrically validated by Bhagat, Cheng, Koneru, Fook, and Chang (2023), the instrument addresses an empirical and methodological need in modern educational technology and instructional design. Blended learning (blended learning) conceptually converges asynchronous digital coursework with synchronous in-person pedagogical engagement, demanding tailored measurement models that move beyond traditional classroom course evaluation instruments.

Constructed via extensive domain literature reviews and synthesizing foundational frameworks from Ginns and Ellis (2007), Dziuban and Moskal (2011), and Owston et al. (2013), the BLCES contains 19 items organized across three primary dimensions: Course Design (9 items), Learning Experience (7 items), and Personal Factors (3 items). Responses are captured using a five-point Likert response format ranging from strongly disagree (1) to strongly agree (5). In its initial psychometric evaluation among undergraduate students in Malaysia, exploratory factor analysis (EFA via principal component analysis with varimax rotation) revealed a three-factor solution explaining 68.06% of the cumulative variance. Subsequent confirmatory factor analysis (CFA) affirmed structural validity with robust goodness-of-fit indices: χ²(149) = 355.71, p < .001, Root Mean Square Error of Approximation (RMSEA) = .06, Comparative Fit Index (CFI) = .96, Goodness-of-Fit Index (GFI) = .92, Normed Fit Index (NFI) = .93, and Tucker-Lewis Index (TLI) = .96. The BLCES exhibits excellent composite reliability (CR: Course Design = .95, Learning Experience = .92, Personal Factors = .71) and high internal consistency (overall Cronbach’s α = .92; subscale α = .71 to .95). As a standardized instrument, the BLCES enables instructional designers, psychometricians, and higher education administrators to conduct diagnostic evaluations, model structural learning determinants, and optimize educational technologies.

Keywords

Blended Learning Course Experience Scale, BLCES, blended learning, course design, learning experience, personal factors, psychometrics, structural validity, higher education assessment, educational measurement, exploratory factor analysis, confirmatory factor analysis.

Authors

The Blended Learning Course Experience Scale was conceived, operationalized, and validated by an international consortium of researchers in educational technology, science education, and instructional systems:

• Kaushal Kumar Bhagat, Ph.D. — Centre for Educational Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India. ORCID: 0000-0002-6861-6819. Corresponding Author Email: [email protected].
• Chia-Hui Cheng, Ph.D. — Graduate Institute of Science Education, National Taiwan Normal University, Taipei, Taiwan.
• Indira Koneru, Ph.D. — Department of e-Learning, ICFAI Business School (IBS), The ICFAI Foundation for Higher Education, Hyderabad, India.
• Fong Soon Fook, Ph.D. — Centre for E-Learning, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia.
• Chun-Yen Chang, Ph.D. — Science Education Center and Graduate Institute of Science Education, National Taiwan Normal University, Taipei, Taiwan. ORCID: 0000-0003-2373-2004.

Purpose

The primary purpose of the Blended Learning Course Experience Scale (BLCES) is to systematically evaluate tertiary students’ perceptions, learning engagement, and affective experiences in hybrid academic courses that integrate synchronous face-to-face instruction with asynchronous computer-mediated learning environments. Modern universities have widely adopted learning management systems (LMS), such as Moodle, Canvas, and Blackboard. However, assessing the success of these implementations has frequently been constrained by general course evaluation surveys that fail to differentiate between pedagogical architecture, experiential benefits, and personal psychological obstacles encountered by students.

The BLCES addresses this methodological gap by capturing the multi-layered interactions characteristic of blended learning environments. From an institutional and pedagogical standpoint, the scale serves several diagnostic, developmental, and empirical purposes:

• Course Evaluation and Quality Assurance: Higher education institutions require validated measurement tools to monitor teaching efficacy across both face-to-face seminars and online LMS modules. The BLCES provides departmental chairs, faculty developers, and curriculum committees with granular data identifying whether curricular gaps stem from instructional clarity, LMS navigation, peer communication, or technological workload.
• Formative Instructional Improvement: Instructors can deploy the scale mid-semester or post-semester to assess whether pedagogical pacing, LMS orientation, online discussion boards, and digital grading rubrics are effectively supporting student comprehension.
• Empirical Research and Structural Modeling: Psychometric researchers and educational technologists utilize the scale as a standardized latent variable measure within structural equation modeling (SEM). It enables investigations examining how course design features predict student retention, academic self-efficacy, cognitive engagement, and semester-end academic performance.
• Targeted Identification of At-Risk Learners: By isolating the “Personal Factors” dimension, academic advisors and instructors can identify students experiencing excessive technology-related anxiety, digital interface friction, or cognitive overload, facilitating timely pedagogical interventions.

Consequently, the BLCES acts not merely as a passive satisfaction instrument, but as an actionable psychometric inventory designed to measure the dynamic cognitive, social, and technological interactions underpinning student success in 21st-century higher education.

Psychological Construct

The psychological construct evaluated by the instrument is the Blended Learning Course Experience, conceptualized as a multi-component psychological and perceptual phenomenon. This construct reflects how students perceive, navigate, and psychologically adapt to the integration of computer-assisted instruction and traditional classroom delivery. Rather than operationalizing course satisfaction as a unidimensional affective state, Bhagat et al. (2023) established that students’ experiences in hybrid curricula are governed by three distinct yet interrelated psychological and perceptual dimensions:

1. Course Design (CD)

The Course Design construct captures students’ evaluations of the instructional architecture, organizational clarity, navigational scaffolding, and pedagogical continuity established by the course instructor. This dimension evaluates instructional coherence across delivery modalities. In a blended context, learning breakdown frequently occurs when face-to-face meetings fail to link conceptually with online modules, or when expectations on online forums, quizzes, and assignments are ambiguous. Course Design assesses the operational transparency of learning objectives, instructor responsiveness and feedback timeliness, LMS interface intuitiveness, and pedagogical pacing. High scores on this dimension signify that learners perceive the course environment as coherent, organized, and structurally aligned with stated academic competencies.

2. Learning Experience (LE)

The Learning Experience construct measures students’ perceived cognitive enrichment, metacognitive gains, skill acquisition, and temporal-spatial agency facilitated by the blended environment. This dimension reflects cognitive presence and autonomy. Items measure how multimedia resources, digital mobile applications, and asynchronous forums expand students’ digital literacy, collaborative communication, and self-directed time-management capabilities. Furthermore, it operationalizes flexibility — the capacity to study anytime, anywhere, and across diverse devices — and examines whether learners perceive that the blended delivery model directly enhanced their mastery of learning materials and overall performance on formal evaluations.

3. Personal Factors (PF)

The Personal Factors construct isolates the internal affective, emotional, and cognitive frictions experienced by students when navigating the blended learning environment. Unlike the positive experiential orientation of the preceding dimensions, Personal Factors evaluates computer anxiety, technical frustration, cognitive burden, and feelings of disproportionate temporal strain. In hybrid courses, students must exercise high levels of self-regulation and digital competence. When students lack digital readiness or experience interface difficulty, anxiety and perceived effort increase. Items in this subscale measure negative psychological friction, including course-induced anxiety, difficulties operating LMS technology, and feelings that the hybrid structure demands excessive cognitive effort relative to conventional courses.

Theoretical Framework

The conceptual underpinning of the Blended Learning Course Experience Scale synthesizes several prominent paradigms in educational psychology, cognitive science, and distance education:

The Community of Inquiry (CoI) Framework

The BLCES is heavily informed by the Community of Inquiry (CoI) framework established by Garrison, Anderson, and Archer (2000). The CoI model posits that meaningful deep learning in computer-mediated environments occurs at the intersection of three core presences: Teaching Presence, Cognitive Presence, and Social Presence. Within the BLCES:

• Course Design reflects Teaching Presence, capturing direct instructional design, organization, setting of curriculum boundaries, and ongoing instructional feedback.
• Learning Experience reflects the convergence of Cognitive Presence (exploration, integration, and resolution of ideas via multimedia and reflective study) and Social Presence (peer and instructor communication via online collaborative forums).

Transactional Distance Theory

Formulated by Michael G. Moore (1993), Transactional Distance Theory proposes that distance education involves a cognitive and pedagogical separation that creates a psychological space of potential misunderstanding. This transactional distance is determined by three variables: Dialogue, Structure, and Learner Autonomy. The BLCES operationalizes this balance: an optimized Course Design reduces transactional distance by establishing clear structure and frequent feedback dialogue, while the Learning Experience dimension assesses whether the student possesses the autonomy to manage asynchronous learning tasks without becoming disoriented or isolated.

Cognitive Load Theory and Technology Acceptance

The inclusion of the Personal Factors dimension aligns with John Sweller’s Cognitive Load Theory and the Technology Acceptance Model (TAM) (Davis, 1989). When learning technologies are unintuitive or disorganized, extraneous cognitive load increases, diverting working memory capacity away from germane cognitive processing. Furthermore, low perceived ease of use induces technological anxiety. The BLCES captures this dynamic by measuring how technological difficulty and anxiety contribute to perceived student burden, operationalizing the friction points that impede academic progress in hybrid education.

Validity

The psychometric validity of the Blended Learning Course Experience Scale (BLCES) was established through an empirical validation study involving undergraduate students enrolled in blended courses at a public university in Malaysia (Bhagat et al., 2023). Multiple forms of validity evidence were documented:

Content and Face Validity

The initial pool of items was developed following a systematic literature review of validated instruments measuring online and blended environments, particularly drawing upon Ginns and Ellis (2007), Dziuban and Moskal (2011), and Owston et al. (2013). The drafted items underwent expert panel review consisting of educational technologists, instructional designers, and psychometricians. Feedback regarding item clarity, cultural appropriateness, theoretical alignment, and construct representation led to iterative refinements, yielding the finalized 19-item version.

Structural / Construct Validity

Structural validity was assessed using Confirmatory Factor Analysis (CFA) with maximum likelihood estimation. The three-factor measurement model exhibited good fit with the observed data across multiple standard indices:

• Chi-Square / Degrees of Freedom: χ²(149) = 355.71, p < .001 (χ²/df = 2.387), meeting the recommended threshold of < 3.0.
• Root Mean Square Error of Approximation (RMSEA): .06, falling well below the conventional .08 cutoff, indicating acceptable approximation error.
• Comparative Fit Index (CFI): .96, exceeding the rigorous .95 benchmark for superior model specification.
• Tucker-Lewis Index (TLI): .96, demonstrating exceptional model fit adjusted for model complexity.
• Goodness-of-Fit Index (GFI): .92, surpassing the .90 benchmark.
• Normed Fit Index (NFI): .93, confirming robust baseline comparative fit.

Convergent Validity

Convergent validity was evaluated by assessing factor loadings, Composite Reliability (CR), and Average Variance Extracted (AVE):

• Factor Loadings: In exploratory analyses, all retained items exhibited factor loadings exceeding .70. In confirmatory models, standardized regression weights across indicators remained strong and statistically significant (p < .001).
• Composite Reliability: Values reached .95 for Course Design, .92 for Learning Experience, and .71 for Personal Factors. All constructs met or exceeded the established psychometric threshold of .70 (Hair et al., 2006).
• Average Variance Extracted: Course Design (AVE > .50) and Learning Experience (AVE > .50) demonstrated strong variance capture relative to measurement error. While the AVE for Personal Factors fell marginally below .50, its composite reliability of .71 supports adequate construct representation given the parsimonious three-item structure.

Discriminant Validity

Discriminant validity was established via the Fornell-Larcker criterion. The square root of the AVE for each construct exceeded the inter-construct correlation coefficients between that latent construct and all other latent dimensions in the model. This confirmed that Course Design, Learning Experience, and Personal Factors represent statistically distinguishable constructs rather than overlapping dimensions of a generalized satisfaction artifact.

Reliability

The BLCES exhibits high internal consistency across both the global scale and its distinct subscales. Reliability coefficients reported by Bhagat et al. (2023) demonstrate that the scale provides stable measurement of blended learning perceptions:

• Full Instrument Internal Consistency: The overall 19-item scale demonstrated an exceptional Cronbach’s alpha coefficient of α = .92, indicating high internal consistency across the complete battery of items.
• Course Design Subscale (9 items): Achieved a Cronbach’s alpha of α = .95 and a Composite Reliability (CR) of .95. These values reflect high measurement precision, indicating that items uniformly capture instructional design, organizational clarity, and pedagogical feedback.
• Learning Experience Subscale (7 items): Achieved a Cronbach’s alpha of α = .92 and a Composite Reliability (CR) of .92, confirming that items evaluating cognitive gains, LMS utility, digital literacy, and spatial-temporal autonomy measure the underlying construct with minimal error variance.
• Personal Factors Subscale (3 items): Achieved a Cronbach’s alpha of α = .71 and a Composite Reliability (CR) of .71. Given that internal consistency estimates are sensitive to item quantity, a reliability coefficient of .71 for a three-item affective scale satisfies psychometric standards for survey research (Nunnally & Bernstein, 1994; Hair et al., 2006).

Overall, the reliability metrics demonstrate that the BLCES minimizes measurement error, providing dependable scores for both group-level research comparisons and programmatic evaluation in higher education institutions.

Factor Analysis

The structural validation of the BLCES was conducted in two sequential analytical phases: an Exploratory Factor Analysis (EFA) to discover the underlying dimensionality, followed by a Confirmatory Factor Analysis (CFA) to verify the factorial validity on the sample data.

Exploratory Factor Analysis (EFA)

An exploratory principal component analysis (PCA) with orthogonal varimax rotation was performed on the initial item pool. Prior to factor extraction, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity confirmed that the correlation matrix was factorable. Using the Kaiser criterion (eigenvalues > 1.0) and scree plot examination, a distinct three-factor structure emerged:

• Cumulative Variance Explained: The three extracted factors collectively accounted for 68.06% of the total variance, surpassing the 60% standard recommended for social science measurement.
• Item Retention Criteria: Following conservative psychometric guidelines (Hair et al., 2006), an item retention threshold was set requiring standardized factor loadings greater than .70 on the primary factor, with minimal cross-loadings onto secondary factors.
• Final Factor Allocation: 19 items successfully satisfied these criteria. Factor 1 (Course Design) captured 9 items, Factor 2 (Learning Experience) captured 7 items, and Factor 3 (Personal Factors) captured 3 items.

Confirmatory Factor Analysis (CFA)

To statistically evaluate whether the three-factor model fit the observed covariance matrix, CFA was conducted. Model parameters were estimated using maximum likelihood procedures. The resulting fit indices verified that the three-factor model adequately reproduced the empirical data:

Instrument / Measurement Tool

The technical specifications and administration protocol for the BLCES are summarized below:

• Instrument Name: Blended Learning Course Experience Scale (BLCES)
• Authors / Developers: Kaushal Kumar Bhagat, Chia-Hui Cheng, Indira Koneru, Fong Soon Fook, and Chun-Yen Chang (2023)
• Instrument Type: Self-report psychometric questionnaire / survey inventory
• Target Population: Undergraduate and graduate students enrolled in blended or hybrid higher education courses (adults aged 18 and older)
• Administration Mode: Electronic administration (computer-assisted web survey, mobile web form, or LMS-integrated questionnaire); paper-and-pencil delivery is also feasible
• Completion Time: Approximately 5 to 10 minutes
• Total Items: 19 items
• Dimensional Structure: Three subscales:
  • Course Design (CD): 9 items (CD1 through CD9)
  • Learning Experience (LE): 7 items (LE1 through LE7)
  • Personal Factors (PF): 3 items (PF1 through PF3)
• Response Scale: 5-point Likert scale:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Neutral
  • 4 = Agree
  • 5 = Strongly Agree
• Scoring Procedures:
  • Subscale Mean Scores: Calculated by summing the scores of items within each subscale and dividing by the number of items in that subscale (e.g., Course Design = sum of CD1-CD9 / 9; Learning Experience = sum of LE1-LE7 / 7; Personal Factors = sum of PF1-PF3 / 3).
  • Directionality & Interpretation: Higher scores on Course Design (CD) and Learning Experience (LE) indicate greater pedagogical clarity, effective instructional organization, and enriched learning autonomy. Items in the Personal Factors (PF) subscale measure negative psychological experiences (anxiety, technological difficulty, disproportionate effort). In structural analyses, Personal Factors may be analyzed as an independent hindrance construct or reverse-scored (where 1 = 5, 2 = 4, 3 = 3, 4 = 2, 5 = 1) if computing a composite positive course experience index.

Permissions & Fee and Test Year

The Blended Learning Course Experience Scale (BLCES) was published in 2023 in the peer-reviewed journal Interactive Learning Environments by Taylor & Francis.

• Commercial Use: Prohibited without explicit written agreement from the copyright holder and authors. Commercial educational platforms, commercial corporate learning vendors, and proprietary software developers must obtain formal authorization.
• Academic and Non-Commercial Research Use: The scale items were published within the appendix of the original scientific publication (Bhagat et al., 2023, p. 3981). Academic researchers, university instructors, and institutional evaluators may utilize the instrument for non-commercial scholarly research and quality assurance purposes, provided appropriate attribution is cited.
• Fees: There is no fee associated with the educational, scientific, or research administration of the scale.
• Permissions & Contact: For inquiries regarding translation rights, large-scale commercial deployments, or research permissions, contact the corresponding author, Dr. Kaushal Kumar Bhagat, at [email protected], or request permission via Taylor & Francis Group.

References

The following publications document the development, validation, and theoretical context of the BLCES:

• Bhagat, K. K., Cheng, C.-H., Koneru, I., Fook, F. S., & Chang, C.-Y. (2023). Students’ Blended Learning Course Experience Scale (BLCES): Development and validation. Interactive Learning Environments, 31(6), 3971–3981. https://doi.org/10.1080/10494820.2021.1946566
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• Dziuban, C., & Moskal, P. (2011). A course evaluation style guide: A new perspective on student satisfaction. Educause Review, 46(6), 1–13.
• Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6
• Ginns, P., & Ellis, R. (2007). Quality in blended learning: Exploring the relationships between on-line and face-to-face technologies and student learning. British Journal of Educational Technology, 38(1), 53–64. https://doi.org/10.1111/j.1467-8535.2006.00652.x
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
• Moore, M. G. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance education (pp. 22–38). Routledge.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Owston, R., York, D., & Murtha, S. (2013). Student perceptions and achievement in a blended learning course: An examination of achievement, student characteristics, and blended course design. Internet and Higher Education, 18, 38–46. https://doi.org/10.1016/j.iheduc.2012.12.003
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Items of the Scale