This work builds on complexity theory in order to identify different types of learners that use video-based learning (VBL). VBL has great value as an educational tool which has already been identified in various contexts. This study combines learners' demographics with learners' experience in a conceptual model to explain the adoption of VBL technologies. We test and validate the proposed model through a survey on 260 VBL users, by implementing the data analysis tool fsQCA (fuzzy-set Qualitative Comparative Analysis). The findings indicate eight configurations of learners' demographics and learners' experience that lead to high intention to adopt VBL. The results take a step further the literature of VBL by taking a different approach and implementing a different methodology, which has recently started to receive increasing attention. We also offer theoretical and practical implications by identifying distinct types of learners that have high behavioral intentions towards VBL.
The implementation of learning videos as educational tools is increasing rapidly the
past years. Researchers focus on video-based learning (VBL), which is defined as the
learning process of acquiring defined knowledge, competence, and skills with the
systematic assistance of video resources [
The study examines the following contingent variables: gender, age, education, and previous experience with learning videos, (i.e., times watched learning videos and minutes spent watching a learning video). The study also considers learners’ behavioral intention to adopt VBL. To this end, we propose a conceptual model and aim to explore the causal patterns of factors that stimulate learners to adopt VBL. The goal of this Copyright © 2016 for the individual papers by the papers' authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
SE@VBL 2016 workshop at LAK’16, April 26, 2016, Edinburgh, Scotland study is to detect specific configurations that explain VBL adoption, and create a typology of VBL learners. Thus, the study addresses the following research question:
What configurations of demographic characteristics and experience with VBL, predict high behavioral intentions to adopt VBL?
The identification of the aforementioned configurations may aid universities and
colleges to better target their learners when they offer VBL for their courses. To answer
the research question we employ complexity theory and configurational analysis using
fuzzy set qualitative comparative analysis (fsQCA) [
The paper has the following structure. Section 2 reviews the related literature and presents the conceptual model. Section 3 described the research methodology and the data used to evaluate the proposed model. Section 4 presents the empirical findings and section 5 concludes the study by discussing ideas for future work in the area of VBL.
The adoption and acceptance of various e-learning tools has been examined with
different adoption models and theories, such as UTAUT2, TPB, and SCT. Previous studies
have identified several important factors as antecedents of intention to adopt e-learning
technologies [
Further, previous experience refers to how many times a learner has used videos for
learning the past six months and for how long the learner watched the video. Prior
research on e-learning and VBL uses various symmetric tests (e.g., multiple regression
analysis) to explain adoption and examines these factors as moderators or control
variables. In learning adoption and continuance research models, split groups analyses also
pinpoint that there are significant differences between groups of learners that are
considered experienced compared to those without any prior experience [
Despite the aforementioned studies and their findings, demographics and experience are very seldom examined simultaneously; therefore, there is limited understanding on how factors that appertain to learner demographics and experience coalesce to shape behavioral intentions to use VBL applications. Given the fact that there are multiple types of learners engaging with VBL, the purpose of this research is to extract a typology of learners using VBL. Thus, we suggest an alternative approach to tackle this question. The methodological approach used in this study builds on identifying the various combinations among the variables of interest. In other words, these combinations will describe which learners’ characteristics increase intention to adopt VBL and offer different categories of learners. The main differentiating aspect of this approach is that multiple different types of learners can be discovered.
The proposed model is illustrated with a Venn diagram (Figure 1) and presents three sets of constructs and their intersections. The three sets reflect (i) the outcome of interest (i.e., dependent variable) and (ii) two sets of causal conditions to act as predictors (i.e., independent variable). The outcome of interest is learners’ behavioral intention to adopt VBL, and demographic characteristics (i.e., gender, age, education) and experience (i.e., times watched a video, minutes watched a video). The intersections among the three sets of constructs represent factors, which are higher level interactions.
A questionnaire on video based learning yielded the data for this study. The participants responded either in person or online. Questionnaires were distributed for two months in various locations (universities, public areas) and e-mails with digital questionnaires sent to a number of mailing lists of individuals with experience in educational activities through VBL. We targeted about 1000 learners, and 302 responded, out of which only 260 had used VBL the past six months, thus comprising our sample. The sample consists of almost equally men (49.6%) and women (50.4%). Regarding age, the majority (36.2%) were between 21 and 25 years old, followed by people between 26-30 years old (24.4%). The rest were older than 30 years (23.1%), and 20 years old or younger (16.3%). Regarding education, the sample consists almost equally of university graduates (46.3%) and postgraduates (44.7%), while a small percentage (8.9%) were high school graduates.
The study applies fuzzy-set Qualitative Comparative Analysis (fsQCA) using
fs/QCA 2.0 [
fsQCA requires the definition of the outcome and the independent measures. Next, all measures need to be calibrated into fuzzy sets with values ranging from 0 to 1. In detail, the value of 1 stands for the full set membership, while that of 0 stands for the no set membership. Table 1 describes the calibration of the raw data.
Regarding the continuous variable measured with a 7 point Likert scale (i.e.,
Behavioral intention) the scale from 0-1, defines the level of their membership. The
transformation of variables into calibrated sets is done by fsQCA program, by setting three
meaningful thresholds; full membership, full non-membership, and the cross-over
point, which describes how much the case belongs to a set [
The calibration of the discrete variables here is simpler since they only take two values, 1 for the full set membership and 0 for the no set membership. Contrary to the continuous variable, the technique used for the discrete variables is direct calibration. Gender is a binary variable. Regarding education, postgraduates were defined as the full set membership and the rest as the full non-membership. Next, the median of age and experience was computed, thus creating the categories of young and older, and high and low experience in terms of times used VBL and minutes watched a video.
After the calibration, the fsQCA algorithm is applied in order to create a truth table
of 2k rows, where k represents the number of outcome predictors, and each row
represents every possible combination. Next, the truth table needs to be sorted out based on
frequency and consistency [
The outcomes of the fuzzy set analysis for high behavioral intentions to use VBL are
presented in Table 2. In detail, black circles (●) indicate the presence of a condition,
while crossed-out circles (⊗) indicate its absence [
The times a learner used VBL and the minutes he or she watched the learning videos
are key conditions in predicting behavioral intentions to adopt VBL, because they are
present in five out of eight configurations. As presented in table 3, fsQCA evaluates the
causal configurations with the greatest raw coverage [
In detail, for solution 1 the raw coverage is 0.17, which suggests that 17% of the young users of learning videos who are high school or university graduates, have high experience with learning videos, and watch the videos for over 10 minutes have high intentions to adopt VBL. The raw coverage for solution 2 is 0.16, which suggests that 16% of women who are high school or university graduates, and watch learning videos for over 10 minutes have high intentions to adopt VBL. Finally, solution 3 with raw coverage 0.13, suggests that 13% of young males, who have high experience with learning videos, and watch the videos for over 10 minutes, have high intentions to adopt VBL. The following table (Table 3) describes the findings for each solution. 1. Young graduates that have watched learning videos more than ten times and for over ten minutes 2. Female graduates that watch learning videos for more than minutes. 3. Young males that have watched learning videos more than ten times and for over ten minutes 4. Old postgraduate males that have watched learning videos more than ten times. 5. Old postgraduates that have watched learning videos more than ten times and for over ten minutes 6. Young females that have watched learning videos more than ten times and for less than ten minutes. 7. Young postgraduate males that watch learning videos for over ten minutes 8. Old male graduates that watch learning videos for less that ten minutes
This study examines the behavioral intention of users to adopt video based learning. The study analyzes how users’ demographic characteristics, such as gender, age, education, combine with experience with learning videos in order to predict intention to adopt VBL. To this end, a conceptual model is proposed in order to explain the aforementioned relationships. The findings describe the different types of users that may have high behavioral intentions to adopt VBL. This research contributes to the video based learning literature. This study adopts a novel analysis methodology (i.e., fsQCA) and offers complex but parsimonious patterns, on which the antecedents may be present or absent, suggesting how the various characteristics combine to explain users’ behavior.
Previous studies explain users’ behavioral intentions by focusing on the main
effects of various antecedents on one or more dependent variables. However, the different
factors may coexist and various combinations may lead to the same outcome. For
example, being highly educated (i.e., postgraduate) does not always suggest high
behavioral intentions, depending on gender, age, and experience. This research makes a step
towards creating new theories in VBL, as it performs configurational analysis based on
individual-level data from users of learning videos, which has been proven appropriate
for theory building [
Future research should examine other variables as well, which have been proven to influence the behavior of VBL users. Such factors, combined with demographics and experience may be able to better predict behavior and while suggesting where researchers and practitioners should focus. In addition, future studies may examine a different outcome of interest, such as the actual use of learning videos. Finally, future research should apply and evaluate the proposed model and method on specific e-learning tools (e.g., flipped classroom, MOOCs).
Our thanks to thank the Norwegian Research Council for its financial support under the projects FUTURE LEARNING (number: 255129/H20) and SE@VBL (number: 248523/H20).