16. ICT in Education and Training
Paper
Does Anxiety in the Use of Computers of Adult Female Distance Learning Students Hinder Their Academic Self-Efficacy?
Ioulia Televantou1, Elena Papanastasiou2, Danxia Chen1
1European University Cyprus, Cyprus; 2University of Nicosia, Cyprus
Presenting Author: Televantou, Ioulia
Online pedagogical practices highlight their potential in improving availability and inclusiveness, especially for individuals with atypical needs (Khan et al., 2022). In this respect, adults comprise the largest audience for online distance education, since the latter provides an opportunity for flexible and continuous learning (Moore & Kearsley, 2011). Still, there exists factors challenging them to engage in online educational; female adult learners have been found to be an especially vulnerable subset of this population (Kara et al., 2019). Individual acceptance and usage of new technologies can be studied using the Technology Acceptance Model (TAM; Davies et al., 1989). According to the TAM, the two key factors in determining the users’ attitudes towards an e-learning system, and consequently, the actual system use, are perceived usefulness (PU) and perceived ease of use (PEOU). Perceived Usefulness (PU) is an individual’s view that the use of a specific system can enhance work performance (Liaw & Huang, 2013). Perceived Ease of Use (PEOU) is the extent to which an individual believes the use of a certain technology system will not require so much effort to be achieved.
The present study evaluates the validity of TAM in the context of e-learning adoption of adult female postgraduate students in a higher education distance learning course in quantitative research methods. We investigate whether PU and PEOU predict users' overall satisfaction with the system's usage. Furthermore, we explore whether students' Computer Anxiety has an effect on PU and PEOU. Importantly, we test whether students' Academic Self-Efficacy can be explained by the two factors underlying the e-learning adoption, PU and PEOU. In this respect, we propose that, in addition to outcomes related to the user experience, namely, Satisfaction from the use of LMS, affective outcomes, namely Academic Self-Efficacy, may also be explained be external factors using the TAM framework. We investigate the direct effect of Computer Anxiety on learners' Academic Self-Efficacy and the indirect effect through PEOU and PU. Our hypothesis is that the effect of Computer Anxiety on ASE will be fully mediated by the two main factors of TAM, namely PU and PEOU. In our models, we control for the perceived quality of the Technical Support for the use of the LMS.
Methodology, Methods, Research Instruments or Sources UsedMethods
Data Sample
The present study uses cross-sectional survey data from a sample of 430 first-year postgraduate students at a Distance Learning program of a private university in Cyprus. The data were collected as part of a quantitative methods course, with a focus on survey research. Our sample consisted mainly of women (371, 85.5%), but there was a very small proportion of men, as well (59 men, 13.6%). Given the focus of our analysis, we decided to listwise exclude men from our sample. The mean age of our participants was 30.46 years old (Mean = 30.46,S.D.=7), with the minimum age being 22 years old, and the maximum 54 years of age. The vast majority of our participants came from Greece (423, 97.5%), while only four came from Cyprus (1%), and two (.5%) from elsewhere. Notable, more than half of our sample were working full-time (264 participants, 60.8%), 88 (20.3%) were working part-time, and 82, 18.9% were not working at all.
Measures
The two key factors that are present in all studies using the TAM model is Perceived Usefulness (PU) and PEOU (Perceived Ease of Use); these were measured by scales proposed by Sanchéz & Hueros (2010), appropriately adopted and translated in the Greek language. Technology Support scale was also taken from the same study. Perceived Satisfaction and Computer Anxiety were taken from Liaw and Huang (2013). Academic Self-Efficacy was assessed using the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich et al., 1991).
Procedures
The data were collected during two consecutive semesters (Fall/Spring) using an online questionnaire that was administered to all students of a graduate distance learning course on designing and contacting survey research. Ethical approval for the conduction of this study was obtained from the Cyprus Bioethical Committee.
Statistical Analysis
We used Structural Equation Modelling (SEM) and Mplus Statistical package (Muthén & Muthén, 2017) to answer our research questions. Before mapping the causal relationships assumed between our contrasts, we verified the construct validity of the scales using Confirmatory Factor Analysis (CFA). Treatment of missing data in our sample involved the use of the default approach in Mplus, namely Full Information Maximum Likelihood (FIML; Lee & Shi, 2021). For assessing model fit we used sample size independent fit indices (Marsh et al., 2015): The Tucker-Lewis and Comparative Fit Indices, TLI and CFI respectively, and the Root-Mean-Square Error of Approximation (RMSEA).
Conclusions, Expected Outcomes or FindingsResults/Conclusions
Confirmatory Factor Analysis verified the assumed latent structure of our measures, and, overall our analysis verified the TAM. In extending the TAM framework, we modelled Academic Self-Efficacy (ASE) as another outcome in our model and we considered its relationship with the two main factors underlying TAM and technology adoption, namely Perceived Usefulness and Perceived Ease of Use. Both of them positively predicted ASE; their effects though were substantially smaller than the corresponding effects of Satisfaction. In considering the effect of Computer Anxiety on ASE, we considered both the direct effect and indirect effects through Perceived Usefulness and Perceived Ease of Use. However, the former was not statistically significant (β = .011,SE=.046) and was therefore not kept in the final model.
Does Technical Support Compensate for the Negative Effect of Computer Anxiety?
In our structural model, we assumed a one-directional relationship between computer anxiety and technical support, modelling a causal path from the former to the latter (Figure 1). Thus, we considered the indirect effects of Computer Anxiety on Perceived Usefulness and Perceived Ease of Use through Technical Support. Estimates were both positive and statistically significant. The total effect of Computer Anxiety on Perceived Ease of Use and Perceived Usefulness is estimated as the sum of direct (β = -.519, SE = .049; β=-.303, SE= .068, respectively) and indirect effects (β = .138, SE =.03; β = .089, SE=.026, respectively). Thus, we conclude that higher perceived quality of Technical Support contributes to the decrease of the negative effect of computer anxiety on the two factors (RH6). In spite of this, it does not lead to the total elimination of this effect.
ReferencesReferences
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. 10.2307/249008
Kara, M., Erdogdu, F., Kokoç, M. and Cagiltay, K., 2019. Challenges faced by adult learners in online distance education: A literature review. Open Praxis, 11(1), pp.5-22. https://doi.org/10.5944/openpraxis.11.1.929
Khan, S., Kambris, M. E. K., & Alfalahi, H. (2022). Perspectives of University Students and Faculty on remote education experiences during COVID-19- a qualitative study. Education and Information Technologies, 27, 4141-4169. 10.1007/s10639-021-10784-w
Liaw, S., & Huang, H. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24. 10.1016/j.compedu.2012.07.015
Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-Learning, online learning, and distance learning environments: Are they the same? Internet and Higher Education, 14, 129-135. 10.1016/j.iheduc.2010.10.001
Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Authors.
Pintrich, P.R., Smith, D.A.F., García, T., & McKeachie, W.J. (1991). A manual for the use of the motivated strategies questionnaire (MSLQ). Ann Arbor, MI University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning.
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640. 10.1016/j.chb.2010.06.011
16. ICT in Education and Training
Paper
Teaching Practice in Post-Covid Classrooms and the Reconfiguration of Blended Learning Models
Heike Schaumburg, Anne-Madeleine Kraft, Björn Kröske, Thomas Koinzer
Humboldt-Universität zu Berlin, Germany
Presenting Author: Schaumburg, Heike;
Kraft, Anne-Madeleine
Originating from higher education institutions, blended learning has increasingly been permeating the K-12 education system in recent years (Picciano et al., 2012). Blended learning refers to the combination of face-to-face (F2F) instruction with online learning. It combines F2F and distance teaching and learning (Hrastinski, 2019). For K-12 education, specific didactic potentials are anticipated in blended learning. These range from enhanced incorporation of students' home learning and other non-school environments to the reinforcement of adaptive, individualized, and project-based learning, as well as the promotion of cross-disciplinary competencies such as self-regulated learning or computer- and information-related skills (Powell et al., 2014).
Blended learning is more widespread in education systems in which distance learning has long been established due to structural conditions (e.g. low population density, possibility of home schooling) and the digitalization of schools is advanced, such as Australia, Canada or the U.S. (Graham & Halverson, 2022). In contrast, blended learning was initially not widely adopted in schools in many European countries. It was only with the Covid-19 pandemic, which forced a reorientation during phases of complete or partial school closures, that blended learning approaches were developed and tested. Studies on teaching during the Covid-19 pandemic indicate a significant increase in the use of digital media in some European countries, especially with regard to learning platforms and communication tools, as seen in Germany or Austria (Karpiński et al., 2020). Teachers recognized the potential in blended learning formats and expressed in surveys their intention to continue using newly tested teaching methods even after the end of the pandemic (Nalaskowski, 2023).
Studies on implementations, primarily conducted in U.S. K-12 schools, have identified different models of blended learning. Watson (2008) categorizes a total of seven blended learning models on a continuum ranging from traditional face-to-face classroom instruction to instruction that is entirely conducted online and remotely. Staker and Horn (2012) map out a two-dimensional space with the dimensions of location (brick and mortar vs. remote) and course content (offline vs. online), identifying four blended learning models (rotation, flex, self-blend, enriched virtual). This classification has gained widespread recognition and continues to be referenced in numerous studies (e.g., Li & Wang, 2022).
However, models like the ones proposed by Staker and Horn (2012) have limited applicability to the European context, specifically in Germany. For example, three of the four models (flex, self-blend, enriched virtual) are based on a configuration where substantial portions of the curriculum are exclusively or predominantly provided online, a situation that was rare in European schools at least until the outbreak of the COVID-19 pandemic (European Commission, 2022). Also, K-12 educational institutions transitioning from pure online institutions towards face-to-face learning, as described in Staker and Horn’s ‘enriched virtual’ model are relatively uncommon in Europe, rendering this model even less applicable.Finally, early models like the ones of Staker and Horn are criticized for falling short in considering pedagogical aspects (Graham & Halverson, 2022).
The goal of this study is thus to investigate blended learning models within a European school context. More specifically, the study analyses, which blended learning models have emerged from experiences with the COVID-19 pandemic and how these models are being implemented into regular F2F school practice. Addressing criticisms of early modeling, the analysis incorporates not only physical aspects, such as the arrangement of space and time and the integration of online and offline learning but also aspects related to the design of learning tasks and learning situations.
Methodology, Methods, Research Instruments or Sources UsedAs part of a pilot project, 18 schools in Berlin, Germany, were given the opportunity to break away from traditional face-to-face instruction and, with digital support, create spatially and temporally flexible learning environments. Legal framework conditions, particularly the mandatory attendance for students, were relaxed to provide schools with extensive freedom to develop innovative teaching concepts.
At the end of the first project year, 75 structured interviews were conducted with students, teachers, school administrators, and project coordinators at the participating schools. At the end of the second project year, another brief interview was conducted with teachers or project coordinators at 15 out of the 18 schools to gather information about the current status of the newly developed concepts.
The interviews at both measurement points were analyzed using the method of qualitative content analysis (Mayring, 2015) in an inductive-deductive manner. Location of learning, temporal structure and methodological-didactic focus emerged as key categories to describe and differentiate blended learning concepts. Characteristics of these three categories were binary coded in the next step and then analyzed using hierarchical cluster analysis (Ward method). Finally, the clusters thus identified were contrasted based on the overall dataset to provide a more comprehensive description of the blended learning concepts.
Conclusions, Expected Outcomes or FindingsThe project schools, depending on their existing profiles, digital infrastructure, and educational objectives, took different paths for the implementation of blended learning in their school routines. The following four blended learning models were identified:
Digitally supported home learning: This cluster is characterized by regular cycles (weekly, monthly) where at least one full school day is designated for digitally supported home learning. Students receive prepared tasks through a learning platform for individualized, usually asynchronous, completion. Teachers offer whole-class video conferences and digital consultation hours.
Project learning at external locations: This cluster also involves regularly occurring days that are used for (partly self-guided) field visits in combination with school-based preparation and follow-up. The didactic concept revolves around project-based learning. Digital media are used for documentation, evaluation, and reflection of learning experiences at non-school learning sites as well as consultation between students and teachers, who are overseeing visits to non-school learning sites from a distance.
Digitalization of independent work: In this cluster, blended learning takes place in regularly occurring time slots, which are integrated into the school week. Students usually remain at school and use the time for digitally supported individualized independent learning, working on tasks provided through a school learning platform. Teachers are available on-site as learning advisors. The didactic concept aims at differentiated support and assistance in subject-specific learning.
Flexibilization of project work in space and time: In this cluster, students work on complex, sometimes interdisciplinary project tasks for limited time periods. Starting from the school as the place of learning, students are given the opportunity to learn at home or to visit locations out of school. Learning times can be freely chosen. Digital media are used for communication among students and between teachers and students. Furthermore, the results of project work are often documented as digital products.
ReferencesEuropean Commission (2022). Teaching and learning in schools in Europe during the COVID-19 pandemic. Luxembourg: Publications Office of the European Union.
Graham, C. R., & Halverson, L. R. (2022). Blended Learning Research and Practice. In: Handbook of Open, Distance and Digital Education (pp. 1-20). Singapore: Springer Nature Singapore.
Hrastinski, S. (2019). What do we mean by blended learning?. TechTrends, 63(5), 564-569.
Karpiński et al. (2020). Digital education action plan 2021-2027. Summary of the open public consultation.
Li, S., & Wang, W. (2022). Effect of blended learning on student performance in K‐12 settings: A meta‐analysis. Journal of Computer Assisted Learning, 38(5), 1254-1272.
Mayring, P. (2015). Qualitative Inhaltsanalyse. Grundlagen und Techniken. Beltz. Weinheim, 4, 58.
Nalaskowski, F. (2023). Covid-19 Aftermath for Educational System in Europe. The positives. Dialogo, 9(2), 59-67.
Picciano, A. G., Seaman, J., Shea, P., & Swan, K. (2012). Examining the extent and nature of online learning in American K-12 education: The research initiatives of the Alfred P. Sloan Foundation. The internet and higher education, 15(2), 127-135.
Powell, A., Rabbitt, B., & Kennedy, K. (2014). iNACOL blended learning teacher competency framework. International Association for K-12 Online Learning.
Staker, H., & Horn, M. B. (2012). Classifying K-12 blended learning. Innosight Institute. Retrieved from: http://192.248.16.117:8080/research/bitstream/70130/5105/1/BLENDED_LEARNING_AND_FEATURES_OF_THE_USE_OF_THE_RO.pdf
Watson, J. (2008). Blended learning: The convergence of online and face-to-face education. Promising Practices in Online Learning. North American Council for Online Learning.
16. ICT in Education and Training
Paper
Revisiting Assure Model in the Digital Era
Ji Young Lim1, Seung Yeon Han2
1Seoul Women's College of Nursing (Seoul, South Korea); 2Hanyang Cyber University (Seoul, South Korea)
Presenting Author: Lim, Ji Young;
Han, Seung Yeon
1. Background of the study
1.1. Problem statements regarding digital technologies for education in digital era
With the rapid innovation of digital technology, the digital transformation of education has accelerated, emphasizing the role of digital technologies in teaching and learning more than ever. The use of digital technology (e.g., Kahoot) to enhance interaction in classrooms, employing personalized learning platforms (e.g., ALEKS), and using augmented/virtual reality to enhance the learning presence are no longer exceptional cases but are commonly found in many classes. Thus, digital technology plays a crucial role in improving the efficiency and effectiveness of the teaching and learning environment.
However, Daniela (2019) pointed out that the centrifugal effect of technology can fragment various components of education, such as learning materials, environments, and peer interactions. Empirical studies have also reported that digital usage in education can lead to social and affective challenges (Lemay, Bazelais, & Doleck, 2021). These issues arising from digital technology necessitate strengthening pedagogical perspectives and approaches in instructional design (Daniela, 2019).
In education, digital technologies are emphasized not only as an environment but also as a competence for learners. The Digital Education Action Plan 2021-2027 of the European Commission (2020) highlighted “Enhancing digital skills and competences for the digital transformation” as its second priority. Learner’s digital literacy (Eshet-Alkalai, 2004) significantly impacts learning achievements in technology-based education (Tang & Chaw, 2016). Therefore, in the context of digital education, it is essential to consider digital literacy as a factor influencing learning, and to ensure that the use of technologies in educational processes naturally enhances learners' digital literacy.
1.2. Research idea to address the problem
In this research, we aim to address educational problems arising in the era of digital innovation by enhancing traditional instructional design model, ASSURE, based on technology-related theory.
The ASSURE model (Heinich, Molenda, Russell, & Smaldino, 1999) is an instructional design model to guide the effective integration of media and learning materials into classrooms. It is a generalized instructional design model like the ADDIE and Dick & Carey models, applicable to various situations and contexts. The model, known for its practicality and effectiveness in enhancing learning achievements, has been widely used so far (Kim & Downey, 2016; Lei, 2023).
However, unlike the past when delivery media were predominantly used, recent technologies are characterized by increased complexity and messiness (Ross & Collier, 2016). In this context, inconsiderate adoption of technology without adequately considering learners' readiness or pedagogy can induce techno-stress and may even lead to extraneous cognitive load (Agbu, 2015; Skulmowski & Xu, 2022). Therefore, if the ASSURE model, a widely used instructional design model, is revised to assist in the integration of innovative technologies into education, it is expected to be more beneficial in the digital era.
As a theoretical framework to improve ASSURE, Task-Technology Fit (TTF; Goodhue & Thompson, 1995) can be considered. TTF is defined as “the degree to which a technology assists an individual in performing his or her portfolio of tasks” (p. 216). Applying TTF to learning implies that if there is an appropriate fit between the learner’s digital literacy (individual characteristics), learning activities (task characteristics), and digital technology for education (technology characteristics), the effectiveness of learning is expected to increase.
1.3. Study objectives and research Questions
Building on the limitations of existing instructional design model in the age of innovative technologies, this study aims to revise ASSURE model based on the TTF model. Research questions are as follow:
Q1. Revised ASSURE mode based on the task-technology fit theory (ASSURE-TTF model) is valid?
Q2. Instructional design according to revised ASSURE model con contribute to the integration of innovative technologies into classes?
Methodology, Methods, Research Instruments or Sources Used2. Research design
This study conducted a Model Research (Type II), the design and development research methodology of Richey and Klein (2014). Model research allows variations considering the focus of the study: whether it's the development, validation, or evaluation. As this study aims to improve an existing instructional design model, ASSURE model was revised based on the literature review on the ASSURE model and task-technology fit theory in the initial phase of the research process. The revised model was then reviewed for validity by three instructional design experts (Ph.D.). Then, ASSURE-TTF model was modified based on their feedback. To check the usability and feasibility of the model, a cognitive walkthrough with five elementary school teachers will be conducted at the last phase of the study.
Conclusions, Expected Outcomes or Findings3.1. Findings from the first two phases of the research procedure
Based on the literature review, the ASSURE-TTF model was revised as follows. In most of the steps, design activities are added to the original design activities.
Step A (learner analysis): An analysis of the learner’s digital literacy was added. This provides information about individual characteristics that affect task-technology fit.
Step S (State standards and objectives): The addition of stating standards and objectives for digital literacy was included.
Step S (Select methods, media, and materials): Instead of selecting methods and media, task analysis and decision-making regarding technology fit were included. For the task analysis, teachers first choose the instructional methods, and design a learning task which will be used according to the instructional method. After this, the activities are specified and sequenced. For the decision-making about the technology fit, technologies are mapped with the learning activities. Also, The selected technology is examined for its suitability in achieving digital literacy learning objectives.
Step U (Utilize): Planning to prevent anticipated digital problems was added.
Step R (Require learner participation): This step involves monitoring and solving technical problems and learner problems caused by technology use.
Step E (Evaluate and revise): Evaluation of technology integration and task-technology fit was added.
Three experts reviewed the validity of the revised model. The researchers of this study are now analyzing the expert review to modify the ASSURE-TTF model.
3.2. Expected outcomes
After modifying the ASSURE-TTF model, a lesson plan will be developed by five elementary school teachers according to the instructional design model. Through these cognitive walkthrough methods, the usability of the model will be checked.
ReferencesAgbu, J. F. (2015). Assessing technostress among open and distance learning practitioners: A comparative study. ASEAN Journal of Open Distance Learning, 7(1), 43-56.
Daniela, L. (2019). Didatics of smart pedagogy: Smart pedagogy for technology enhanced learning. Springer.
Eshet-Alkalai, Y. (2004). Digital literacy: a conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia, 13(1), 93-106.
European Commission (2020). Communication from the commission to the European parliament, the council, the European economic and social committee and the committee of the regions: Digital Education Action Plan 2021-2027 Resetting education and training for the digital age.
Goodhue, D., & Thompson, R. L. (1995). Task–technology fit and individual performance. MIS Quarterly, 19(2), 213–236.
Heinich, R.,Molenda,M., Russell, J. D., & Smaldino, S. (1999). Instructional media and technologies for learning (6th ed.). Merrill/Prentice Hall.
Richey, R. C., & Klein, J. D. (2007). Design and development research. Taylor & Francis Group.
Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171-196.
Kim, D., & Downey, S. (2016). Examining the Use of the ASSURE Model by K–12 Teachers. Computers in the Schools, 33(3), 153-168.
Lemay, D. J., Bazelais, P., & Doleck, T. (2021). Transition to online learning during the COVID-19 pandemic. Computers in Human Behavior Reports, 4, 100130.
Lei, G. (2023). Influence of ASSURE model in enhancing educational technology. Interactive Learning Environments, 1-17.
Tang, C. M., & Chaw, L. Y. (2016). Digital Literacy: A Prerequisite for Effective Learning in a Blended Learning Environment?. Electronic Journal of E-learning, 14(1), 54-65.
Ross, J., & Collier, A. (2016). Complexity, mess, and not-yetness: Teaching online with emerging technologies. In T. Anderson (Ed). Emergence and innovation in digital learning. (pp. 17-34). George Veletsianos.
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