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Predicting Student Satisfaction and Perceived Learning Within Online Learning Environments

Listen and learn

Listen and learn : Research paper
Lecture presentation


Tuesday, June 25, 11:45 am–12:45 pm
Location: 121AB

Presentation 2 of 3
Other presentations:
CANCELLED: Social Media Bridging the Distance in Online Education
What Forms Learners' Online Educational Experience: Analyzing the relationships among the Community of Inquiry Framework Elements

Dr. Emtinan Alqurashi  
Student satisfaction is used to measure the quality of online programs while perceived learning is considered an indicator of learning. This research explores the role of learner-learner, learner-instructor and learner-content interaction along with online learning self-efficacy. This research identified what best predicts satisfaction and perceived learning.

Audience: Teacher education/higher ed faculty, Professional developers
Attendee devices: Devices not needed
Focus: Digital age teaching & learning
Topic: Distance, online, blended and flipped learning
Grade level: Community college/university
Subject area: Not applicable, Higher education
ISTE Standards: For Administrators:
Digital Age Learning Culture
  • Ensure instructional innovation focused on continuous improvement of digital age learning.
For Educators:
Learner
  • Stay current with research that supports improved student learning outcomes, including findings from the learning sciences.
Designer
  • Explore and apply instructional design principles to create innovative digital learning environments that engage and support learning.

Proposal summary

Framework

Student satisfaction reflects how students perceive their learning experience and it is considered as one of the five pillars for the evaluation of the quality of online education as identified by the Online Learning Consortium, formerly The Sloan Consortium (Moore, 2005). These pillars can be applied as a framework by educational institutions to evaluate and develop their online programs. The importance of student satisfaction with online learning is well-documented in research, and found to be highly related to students’ dropout rates, determination, motivation and commitment to complete a degree online, and success rates (Ali & Ahmad, 2011; DeBourgh, 1999; Yukselturk & Yildirim, 2008). Similarly, perceived learning has been considered as an indicator of learning, and it is one of the core elements for course evaluation (Wright, Sunal & Wilson, 2006). Students who believe that they learned the course materials extremely well are more likely to be active participants in online classes (Fredericksen et al., (2000). Perceived learning is also highly predictive of students’ grades (Rockinson-Szapkiw et al., 2016). By understanding what affects perceived learning, it helps instructors improve the quality of online courses in terms of course design, delivery, evaluation, etc., in order to ultimately improve the student learning experience (Alavi, Marakas & Youngjin 2002).

Self-efficacy is a key component in student learning and satisfaction. It is defined as “the level of confidence that someone’s have to perform a particular task, activity, action or challenge” (Alqurashi, 2016, p. 45). If a student believes that he/she cannot achieve the results, they will not make any effort to make things happen. On the other hand, students with high self-efficacy do not take difficult tasks as obstacles to avoid, but instead they take it as a challenge to develop their skills, and as a result students could learn well from course materials and be satisfied with the learning experience.

Prior studies that discussed self-efficacy within online learning environments in the context of higher education mostly focused on the technological aspect of self-efficacy, such as Internet self-efficacy, Learning Management System self-efficacy, computer self-efficacy, web-user self-efficacy (Jan, 2015; Kuo, et al., 2014; Martin & Tutty, 2008; Martin, Tutty & Su, 2010; Simmering, Posey & Piccoli, 2009). Previous studies show that students’ self-efficacy for technology has changed over the years (Alqurashi, 2016). College students are becoming more confident in performing web-based activities as years go by and as a result, self-efficacy for using technology is becoming less predictive of student learning experiences. Therefore, there is a need to research self-efficacy not from technological perspective but to focus on students’ confidence in their ability to perform, learn, engage and complete an online course successfully.

Another critical element in online learning is interaction. A number of researchers have emphasized its importance (Abrami et al., 2011; Cho & Kim, 2013; Kuo, et al., 2013; Kuo, et al., 2014). This is mainly because of the essential role interaction play in online formal education, and also because interaction was mostly absent during early stages of distance education (Abrami et al., 2011). However, it is important to note that effective interaction occurs only if learning and instruction were designed and implemented well. It is about quality interaction not just quantity. Interaction refers to the interaction a learner has with course content, class instructor, and their peers. Learner-content interaction is the interaction that occurs between student and the subject matter, and it is a highly-individualized process facilitated by the instructor. Learner-instructor interaction is a two-way communication between learners and the instructor of the course. Learner-learner interaction is a two-way communication between or among learners for the purpose of exchanging information or ideas related to course content. This can occur with or without instructor supervision (Moore, 1989).

Methods

A variety of survey instruments were utilized to collect information about students’ perception of their online learning self-efficacy (OLSE), their interaction with the course content (LCI), course instructor (LII), and other learners (LLI), in order to understand if they significantly predict student satisfaction and perceived learning.

A total of 167 graduate and undergraduate students completed the survey. The participants were taking at least one fully online course from a private mid-sized non-profit university in Western Pennsylvania. Blackboard, a learning management system, was utilized for all their online courses, along with GoToTraining, a web conferencing system was used for synchronous classes (i.e. classes that meet in real time).

Students were asked to complete a survey. There were six scales used in this survey: (1) self-efficacy to complete an online course, (2) learner-instructor interaction, (3) learner-content interaction, (4) learner-learner interaction, (5) student satisfaction, and (6) perceived learning.

The self-efficacy to complete an online course scale, developed by (Shen et al., 2013), asks students how confident they are that they could do certain tasks in an online course. Students rate their level of confidence on a 5-point Likert Scale, where (1) indicates “cannot do at all”, (3) indicates “moderately confident can do”, and (5) indicates “highly confident can do”. High rating scores indicate high self-efficacy and low rating scores indicate low self-efficacy.

The three interaction scales, developed by (Kuo, et al., 2014), ask students to mark the most appropriate number on a 5-point Likert scale, where (1) indicates strongly disagree and (5) indicates strongly agree.

The student satisfaction scale, developed by (Artino, 2007), includes two items to assess student satisfaction with their online course. The survey asks students to mark the most appropriate number on a 5- point Likert scale, where (1) indicates “completely disagree” and (5) indicates “completely agree”. Additionally, the perceived learning scale, developed by (Artino, 2007), asks students to mark the most appropriate number on a 5-point Likert scale next each statement, where (1) indicates “not well at all” and (5) indicates “extremely well”.

Results

Standard Multiple Regression was conducted to determine whether all four independent variables predict student satisfaction. Regression results indicated that the overall model with the four independent variables (online learning self-efficacy, learner-content, learner-instructor, and learner-learner interaction) significantly predicts student satisfaction, R2 = .636, F(4, 162) = 70.691, p < .001. This model accounts for 63.6% of the variance in student satisfaction. After reviewing the beta weights, it was determined that only three variables (online learning self-efficacy, learner-content, and learner-instructor interaction) significantly contributed (p < .001) to this model. Among those significant predictors, learner-content interaction was the strongest and most significant (t = 7.340, p < .001).

Another standard Multiple Regression was conducted to determine whether all four independent variables predict perceived learning. With perceived learning as a dependent variable, regression results indicated that the overall model with four independent variables (online learning self-efficacy, learner-content, learner-instructor, and learner-learner interaction) significantly predicts perceived learning, R 2 = .465, R 2adj =.452, F(4, 162) = 35.184, p < .001. This model accounts for 46.5% of the variance in perceived learning. After reviewing the beta weights, it was determined that only three variables (online learning self-efficacy, learner-content, and learner-instructor interaction) significantly contributed (p < .01) to this model. Among those significant predictors, online learning self-efficacy was the strongest and most significant (t = 4.422, p < .001).

When removing learner-learner interaction from the model, results show that the F value increased, the error was reduced, and the model accounts for 63.6% of the variance in student satisfaction and for 46.3% of the variance in perceived learning. This means that learner-learner interaction has almost no to little effect on the models.

Results also indicated that learner-content interaction explains 12% unique variance in student satisfaction, which is the highest of all significant predictors. This means that the more instructors increase learner interaction with content, the more likely to have satisfied learning experience by students. Followed by online learning self-efficacy, the second highest predictor in student satisfaction, it explains 3.5% unique variance. Learner-instructor interaction is the third highest significant predictor, it explains 3.3% unique variance in student satisfaction.

On the other hand, results indicated that online learning self-efficacy explains 6.5% unique variance in perceived learning, which makes it the highest of all significant predictors. This means that the higher students’ self-efficacy is, the more likely that they will have high perceived learning. Then comes learner-instructor interaction, the second highest predictor in perceived learning, it explains 4.5% unique variance. LCI is the third highest significant predictor, it explains 3.8% unique variance in perceived learning.

Results of self-efficacy suggest that it is more likely to have high student satisfaction and perceived learning rates if students come to an online course with high confidence in the capabilities of getting a good grade, dealing with difficult topics, facing challenges, completing online activities, managing course schedule, planning and evaluating assignments based on rubrics, and meeting course expectations. This study also suggests that it is more likely to have high student satisfaction rates if students find that online course materials: helped them to understand the class content, stimulated their interest for the course, helped relate their personal experience to new knowledge, and were easy to find and access. Additionally, It is also more likely to have a high student satisfaction and perceived learning rates if students had high quality and quantity interactions with their instructor. This includes asking and answering questions, receiving feedback, and participating in online discussions. In online learning environments, instructor’s response and feedback are essential due to the lack of face-to-face communication.

Further research could investigate ways to improve quality of learner-learner interaction and how to design activities that students can highly benefit from. The idea here is not to eliminate learner-learner interaction but to find techniques to improve it.

Importance

Higher education institutions offer many opportunities to take online courses and complete degree programs online. This is to meet the continuous increase in online learning enrollment. As the number of enrollment in online courses in higher education increases (Allen & Seaman, 2017), so does the need for research to identify factors that play an important role in student satisfaction and learning.

The importance of student satisfaction with online learning is well-documented in research, and found to be highly related to students’ dropout rates, determination, motivation and commitment to complete a degree online, and success rates (Ali & Ahmad, 2011; DeBourgh, 1999; Yukselturk & Yildirim, 2008). Similarly, perceived learning has been considered as an indicator of learning, and it is one of the core elements for course evaluation (Wright, Sunal & Wilson, 2006). It is also highly predictive of students’ grades (Rockinson-Szapkiw et al., 2016). Students who believe that they learned the course materials extremely well are more likely to be active participants in online classes (Fredericksen et al., 2000).

There is a limited research on the direct effect that self-efficacy and interaction have on both student satisfaction and their perceived learning within the context of online environments in higher education. In addition, it is still unclear from research if interaction would result to learning the course materials well and satisfaction with course experience. No research was found to use online learning self-efficacy and, learner-content, and learner-instructor, and learner-learner interaction in multiple predictive models to examine whether they predict satisfaction and learning.

Results from this study can support instructors in higher education and instructional designers/technologist to improve planning, designing, developing, and delivering quality online education in order to improve students learning as well as their satisfaction.

References

Abrami, P. C., Bernard, R. M., Bures, E. M., Borokhovski, E., & Tamim, R. M. (2011). Interaction in distance education and online learning: Using evidence and theory to improve practice. Journal of Computing in Higher Education, 23(2-3), 82-103. doi: 10.1007/s12528-011-9043-x.

Alavi, M., Marakas, G. M., & Youngjin, Y. (2002). A comparative study of distributed learning environments on learning outcomes. Information Systems Research, 13(4), 404-415.

Ali, A., & Ahmad, I. (2011). Key factors for determining students' satisfaction in distance learning courses: A study of allama iqbal open university. Contemporary Educational Technology, 2(2), 118.
Allen, E., & Seaman, J. (2017). Digital learning compass: Distance education enrollment report 2017.

Alqurashi, E. (2016). Self-Efficacy in Online Learning Environments: A Literature Review. Contemporary Issues in Education Research, 9(1), 45-52. doi:10.19030/cier.v9i1.9549.

Artino, A. R. (2007). Online military training: Using a social cognitive view of motivation and self-regulation to understand students' satisfaction, perceived learning, and choice. Quarterly Review of Distance Education, 8(3), 191-202.

Cho, M.-H., & Kim, B. J. (2013). Students' self-regulation for interaction with others in online learning environments. The Internet and Higher Education, 17, 69-75. doi: 10.1016/j.iheduc.2012.11.001.

DeBourgh, G. A. (1999). Technology is the tool, teaching is the task: Student satisfaction in distance learning. Paper presented at the Society for Information Technology & Teacher Education International Conference San Antonio, TX. http://files.eric.ed.gov/fulltext/ED432226.pdf

Fredericksen, E., Pickett, A., Shea, P., Pelz, W., & Swan, K. (2000). Student satisfaction and perceived learning with on-line courses: Principles and examples from the suny learning network. Journal of Asynchronous Learning Networks, 4(2).

Jan, S. K. (2015). The relationships between academic self-efficacy, computer self-efficacy, prior experience, and satisfaction with online learning. American Journal of Distance Education, 29(1), 30-40. doi: 10.1080/08923647.2015.994366.

Kuo, Y.-C., Walker, A. E., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distributed Learning, 14(1), 16-39.

Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50. doi: 10.1016/j.iheduc.2013.10.001.

Martin, F., & Tutty, J. I. (2008). Learning management system self-efficacy of online and hybrid learners: Using lmses scale. Paper presented at the Proceeding of the UNC Teaching and Learning with Technology Conference, Raleigh, NC.

Martin, F., Tutty, J. I., & Su, Y. (2010). Influence of learning management systems self efficacy on e-learning performance. i-manager’s Journal on School Educational Technology, 5(3), 26-35.
Moore, J. C. (2005). The sloan consortium quality framework and the five pillars. Newburyport, MA: The Sloan Consortium.
Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1-7. doi: 10.1080/08923648909526659.

Rockinson-Szapkiw, A., Wendt, J., Whighting, M., & Nisbet, D. (2016). The Predictive Relationship Among the Community of Inquiry Framework, Perceived Learning and Online, and Graduate Students’ Course Grades in Online Synchronous and Asynchronous Courses. The International Review Of Research In Open And Distributed Learning, 17(3). Doi: 10.19173/irrodl.v17i3.2203.

Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10-17. doi: 10.1016/j.iheduc.2013.04.001.

Simmering, M. J., Posey, C., & Piccoli, G. (2009). Computer self-efficacy and motivation to learn in a self-directed online course. Decision Sciences Journal of Innovative Education, 7(1), 99-121.
Wright, V. H., Sunal, C. S., & Wilson, E. K. (2006). Research on enhancing the interactivity of online learning. Greenwich, Connecticut: Information Age Publishing Inc.

Yukselturk, E., & Yildirim, Z. (2008). Investigation of interaction, online support, course structure and flexibility as the contributing factors to students' satisfaction in an online certificate program. Journal of Educational Technology & Society, 11(4), 51-65.

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Presenters

Dr. Emtinan Alqurashi, Temple University

Dr. Emtinan Alqurashi is an Assistant Director of Instructional Technology at Temple University’s Center for the Advancement of Teaching. In her role, Dr. Alqurashi facilitates workshops and faculty learning communities, provides consultations, lead the edtech partners program, work on SoTL projects and more. Her research interests include teaching and learning within online environments, instructors’ knowledge and skills in integrating technology, and the use of instructional technology to improve teaching and learning.

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