Video lectures in e-learning

Interactive Technology and Smart Education Video lectures in e-learning: Effects of viewership and media diversity on learning, satisfaction, engagement, interest, and future behavioral intention Jamie Costley, Christopher Henry Lange,

Downloaded by Doctor Christopher Lange At 03:00 29 April 2017 (PT)

Article information: To cite this document: Jamie Costley, Christopher Henry Lange, (2017) "Video lectures in e-learning: Effects of viewership and media diversity on learning, satisfaction, engagement, interest, and future behavioral intention", Interactive Technology and Smart Education, Vol. 14 Issue: 1, pp.14-30, doi: 10.1108/ ITSE-08-2016-0025 Permanent link to this document: http://dx.doi.org/10.1108/ITSE-08-2016-0025 Downloaded on: 29 April 2017, At: 03:00 (PT) References: this document contains references to 68 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 193 times since 2017*

Users who downloaded this article also downloaded: (2017),"Principles of successful implementation of lecture recordings in higher education", Interactive Technology and Smart Education, Vol. 14 Iss 1 pp. 2-13 http://dx.doi.org/10.1108/ITSE-09-2016-0031 (2017),"Affect-based adaptation of an applied video game for educational purposes", Interactive Technology and Smart Education, Vol. 14 Iss 1 pp. 31-49 http://dx.doi.org/10.1108/ ITSE-07-2016-0023 Access to this document was granted through an Emerald subscription provided by Token:JournalAuthor:84D49D02-B365-4B3E-9FAC-6D5B9DC18C5F:

For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.

About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download.

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1741-5659.htm

ITSE 14,1

14 Received 4 August 2016 Revised 23 October 2016 Accepted 23 October 2016

Video lectures in e-learning Effects of viewership and media diversity on learning, satisfaction, engagement, interest, and future behavioral intention Jamie Costley Department of English Education, Kongju National University, Gongju, South Korea, and

Christopher Henry Lange Downloaded by Doctor Christopher Lange At 03:00 29 April 2017 (PT)

Liberal Arts Department, Joongbu University, Geumsan-gun, South Korea

Abstract Purpose – Because student viewership of video lectures serves as an important aspect of e-learning environments, video lectures should be delivered in a way that enhances the learning experience. The delivery of video lectures through diverse forms of media is a useful approach, which may have an effect on student learning, satisfaction, engagement and interest (LSEI), as well as future behavioral intentions (FBI). Furthermore, research has shown the value that LSEI has on learner achievement within online courses, as well as its value in regards to student intention to continue learning in such courses. The purpose of this study is to investigate the relationships between media diversity, LSEI and FBI in hopes of enhancing the e-learning experience. Design/methodology/approach – This study surveyed a group of students (n ⫽ 88) who participated in cyber university classes in South Korea to investigate the correlations between media diversity and lecture viewership, effects of lecture viewership on LSEI and FBI, effects of media diversity on LSEI and FBI as well as the correlation between LSEI and FBI. Findings – Results show no relationship between media diversity and viewership. Both lecture viewership and media diversity were positively correlated with LSEI. However, neither media diversity nor viewership was positively correlated with FBI. Finally, LSEI was positively correlated with FBI. Originality/value – This paper looks at how video lectures affect LSEI. Past research has generally looked at learning, satisfaction, engagement and interest as separate entities that are affected by instructional aspects of online learning. Because of their interrelationships with each other, this study combines them as one construct, making a stronger case for their combined association. Keywords Engagement, Learning, Satisfaction, Behavioral intention, Interest, Media diversity Paper type Research paper

Interactive Technology and Smart Education Vol. 14 No. 1, 2017 pp. 14-30 © Emerald Publishing Limited 1741-5659 DOI 10.1108/ITSE-08-2016-0025

1. Introduction E-learning provides learners with autonomy to engage in a wide array of online content delivered through various forms of instructional media (Bouhnik and Marcus, 2006; Kim et al., 2011; Liaw et al., 2007; Lee and Lee, 2015). University students are enrolling in online courses at an increasing rate to take advantage of the benefits they offer, which include access to a wide range of courses and the convenience of studying anywhere and anytime (NCES, 2008; Traphagan, 2005). Although student enrollment is on the rise, e-learning does not come without its limitations. Potential drawbacks include lack of interaction between students and instructors, as well as feelings of isolation within the learning environment (Al-Qahtani and Higgins, 2013; Cole et al., 2014; Jung, 2000; Lee and Rha, 2009). Challenges that cyber universities face include overcoming this “distance” students feel from the learning environment, which can negatively affect satisfaction and learning through a lack


of interest and loss of motivation (Lee and Rha, 2009; Russo and Benson, 2005). The way in which video lectures are delivered can address this issue by improving perceptions of instructor presence that may compensate for the lack of physical interaction between teacher and students (Oomen-Early et al., 2008). Instructors need to use lecture delivery methods that stimulate learners in a way that promotes successful learning, engagement and interest, as well as overcome issues that potentially lead to dissatisfaction. One way of doing this is through the delivery of diverse media, which can have an effect on cognitive processing, and ultimately lead to a more positive learning experience (Kalyuga et al., 1999; Mayer and Moreno, 2003; Lowe, 1999; Mayer, 2014; Sims and Hegarty, 1997; Rasch and Schnotz, 2009; Schnotz and Rasch, 2005; Sweller, 1999; Sweller et al., 1998; van Merriënboer, 1997). Diverse media may not only improve the learning experience, but may also have a positive impact on students’ decisions to continue taking advantage of such positive learning experiences in the future. The goal of much research has been to increase levels of learning, satisfaction, engagement and interest (LSEI) within e-learning environments. These four factors are related, not only in a sense that they are all associated with the student learning experience, but that relationships have also been found between them. In addition to their association with successful implementation of e-learning, learning and satisfaction have also been commonly linked by researchers (Eom et al., 2006; Levy, 2007, Sun et al., 2008; Zhang et al., 2006). Learning is associated with achievement within an online environment, whereas satisfaction has been found to be a predictor of aspects of learning through its effect on retention as well as its influence on student motivation (Astin, 1993; Bailey et al., 1998; Chute et al., 1999; Edwards and Waters, 1982; Donohue and Wong, 1997). Satisfaction is associated with higher levels of student interest within online courses (Zhan and Mei, 2013). Furthermore, student interest is associated with engagement online, as those that are interested tend to engage more in the course content (Huang, 2003; Koufaris, 2002; Lee, 2009). The importance of engagement within e-learning is evident, as it serves as a necessary prerequisite for learning (Guo et al., 2014). It is apparent that learning, satisfaction, engagement and interest often overlap, appearing to be closely interconnected with each other. Acknowledging the similarities and relationships between these attributes, as well as the benefits they provide to the learning process, it is useful to focus on research that encourages their promotion in an online environment. Student behavioral intentions, specifically intentions to use e-learning, have also been viewed as a key part of the e-learning experience (Giannakos et al., 2015). Awareness of student behavioral intentions is important because it allows instructors to create effective e-learning environments for student continuance of study in that context (Grandon et al., 2005). The environment should be created in a way where students believe it is helping them in their learning process. Research supports this notion by showing that students who view the learning environment as useful also indicate an intention to use e-learning in the future (Liaw. 2008). The idea of intention to use is based on Davis et al.’s (1989) technology acceptance model (TAM), which proposes two specific beliefs, perceived usefulness and perceived ease of use, both of which determine student intention to use technology. This effect has been verified by others who have shown that perceived ease of use has an effect on intention to use, and that perceived usefulness mediates that effect (Venkatesh and Davis, 2000). The significance of this relationship is noteworthy, as intention to use has been known to lead to actual usage (Lee, 2009). Acknowledging the benefits of intention to use within the learning process of Web-based instruction, it would seem appropriate to engage users of video lectures in a way that not only benefits learning but also promotes continued usage in the future so that learning can be enhanced continuously.

Video lectures in e-learning

15

ITSE 14,1


16

2. Current study Although learning, satisfaction, engagement and interest have been previously investigated within e-learning contexts, research has generally viewed them as separate entities (Astin, 1993; Bailey et al., 1998; Chute et al., 1999; Donohue and Wong, 1997; Eom et al., 2006; Guo et al., 2014; Huang, 2003; Koufaris, 2002; Lee, 2009; Levy, 2007; Sun et al., 2008; Zhan and Mei, 2013; Zhang et al., 2006). Because of their interrelationship with each other, this study differs from other studies by combining them as one construct (LSEI), making a stronger case for their impact on learning through their combined association. Additionally, previous research has generally looked at ways of increasing learning, satisfaction, engagement and interest through instructional design decisions based on controlling the interaction within smaller online environments (Arbaugh 2000; Costley and Lange, 2016; Shea et al., 2003; Sun et al., 2008; Swan, 2001; Thurmond et al., 2002). However, large cyber university courses often lack sufficient interaction between students and instructors. This lack of interaction places an even greater importance on the promotion of LSEI through the delivery of video lectures. Investigating behavioral intention to use e-learning is also important because it is linked to continuous usage among students in online courses. Past research has explored relationships between intention to use and aspects of video lectures. However, the current study digs a little deeper by observing a relationship between LSEI and future behavioral intentions (FBI). Additionally, the delivery of diverse media within video lectures is investigated in hopes of identifying a useful strategy to enhance levels of LSEI, FBI and video lecture viewership. To promote actual viewership of video lectures, further analysis is focused on the effects of watching video lectures on student levels of LSEI and FBI. It is the aim of this study to promote a positive learning experience through the delivery of video lectures, specifically delivery involving diverse forms of media. Based on this information, the following hypotheses are proposed: H1. Media diversity is positively correlated with the amount of lectures watched. H2a. Media diversity is positively correlated with LSEI. H2b. Media diversity is positively correlated with FBI. H3a. Amount of lectures watched is positively correlated with LSEI. H3b. Amount of lectures watched is positively correlated with FBI. H4. LSEI is positively correlated with FBI. 3. Theoretical background Since the implementation of video lectures as part of e-learning lessons, instructors have attempted to apply various techniques to increase the levels of student viewership of the lectures. Incorporating various forms of media with the video lectures serves as an approach that may help to increase viewership of the lectures. A limited amount of research has looked into this phenomenon in an attempt to support media diversity within e-learning environments. Although no research was found that specifically linked media diversity to the amount of videos watched, research has been found that attributes media diversity to peaks in viewership within the video lectures themselves. Kim et al. (2014) found that viewership peaks occur when media formats such as slides or notes replaced the talking head in the video. Guo et al. (2014) found that the lectures that incorporated a video of someone talking in addition to slides and screenshots showed a higher rate of viewership than lectures with slides and screenshots alone. Although more research is needed in regards to this issue, the following hypothesis is made for this study based on previous research findings:


H1. Media diversity is positively correlated with the amount of lectures watched. Numerous studies have discussed the advantages of media diversity within video lectures. Specifically, media diversity has been linked to better comprehension, as well as higher levels of learning, satisfaction, engagement and interest. Klass (2003), for example, showed that combining video lectures with other forms of media such as text and graphics is helpful for understanding concepts and procedures that may not be easily understood with video alone. Kim et al. (2011) showed that using a variety of media integration tools such as images, graphics, audio, video clips and in-video text correlated with higher levels of perceived learning. Research has also shown that students are more satisfied with lecture captures that integrate various forms of media, including slides along with audio of the professors, rather than a simple video/audio of the professors (Bongey, 2006; Brecht and Ogilby, 2008). Student satisfaction is often preceded by engagement, which is also affected by media diversity. Zhang et al. (2006), for example, explained that interactive video lectures that use richer media, such as slides with text, increases learner engagement, which further leads to satisfaction and higher learning outcomes. Michelich (2002) adds support to the findings of Zhang et al. (2006) in regards to satisfaction, stating that instructor use of a combination of multimedia along with traditional delivery of text-based lectures can increase levels of engagement (Michelich, 2002). Engagement has often been associated with student interest, which has also been shown to have a connection to media diversity. For example, Rawat et al. (2014) explain that using only slides within a cyber lecture can create boredom, whereas slides with video of the professor integrated into them tend to create more interests among students. The overwhelming amount of research that supports using media diversity to increase levels of LSEI leads to the following hypothesis within the current study: H2a. Media diversity is positively correlated with LSEI. Because research has shown that media diversity has been linked to increased levels of LSEI, it would be reasonable to conclude that learners would also show higher levels of intent to continue using e-learning when a variety of media formats are delivered within the lecture. A limited amount of research provides support for this notion. Liaw (2008) looked at relationships between e-learning usefulness and multimedia lecture delivery, which consisted of video files complemented by slides, PDFs and document files. Liaw (2008) makes the case for an effect of diverse media delivery on user intention indirectly through perceived usefulness. The results showed that perceived usefulness, which is related to intention to use, is influenced by lecture delivery involving the use of multimedia. Liu et al. (2009) found similar results by looking at the influence of different media, referred to as presentation type, to represent richer content presentation on intention to use e-learning. Their results concluded that using integrated media consisting of video, audio and text was positively correlated with perceived usefulness. Although no research was found that showed a direct relationship between delivery diversity and student behavioral intention to use e-learning in the future, the fact that relationships were found through mediation leads to the formation of the following hypothesis: H2b. Media diversity is positively correlated with FBI. Positive effects have been found on the relationship between watching video lectures and LSEI. Generally speaking, learners have described viewing video lectures as enjoyable, satisfying, motivating and effective for learning (Traphagan et al., 2010). Research reports that students’ perception of learning is positively influenced by lecture viewership (Bongey, 2006; Traphagan, 2005). Specifically regarding the amount of video lectures students view, studies have found that watching more videos leads to an increase in learning. For example,


17

ITSE 14,1


18

Chandra (2007) showed that watching portions of video lectures more than once for review purpose had a positive effect on learning. Satisfaction levels have also been found to increase because of video lecture viewership. Owston et al. (2011) showed that when video lectures replicate face-to-face environments, they are associated with higher levels of satisfaction. A relationship between video lectures and engagement was found by Guo et al. (2014), who showed that when students view shorter videos, their levels of engagement increase. Video lectures may have a negative effect on student interest if students perceive them as dull, not helpful or non-engaging (Danielson et al., 2014). Danielson et al. (2014) found that video lectures can capture student interest by moving quickly, containing relevant information for course success and containing information that students are unable to find elsewhere. Brecht (2012) tied video lecture viewership to student interest by stating that video lectures are effective in capturing student interest by providing a sense of intimacy because of less environmental distractions than face-to-face offline lectures. Based on these findings, the current study hypothesizes the following: H3a. Amount of lectures watched is positively correlated with LSEI. Because engaging students to actually view video lectures is important for the learning process, it would be beneficial to see if an increase in video lecture viewership is related to higher student intention to continue in e-learning situations. Giannakos et al. (2015) related video lecture viewing length to student intention to continue using aspects of e-learning by showing that students who watched longer videos also showed intention to use video lectures in the future. However, research is mixed when it comes to relating past experience of watching videos with intention to use video lectures in the future. Giannakos and Vlamos (2013) claim that students who have more experience watching videos in the past also show higher levels of intention to use e-learning environments that support video lectures in the future. This was contradicted by a later study which found that there was no significant effect on past experience with intention to use in the future (Giannakos et al., 2015). More research specifically focusing on the relationship between the amount of video lectures viewed and intention to use is needed, but based on existing research, the following claim is made: H3b. Amount of lectures watched is positively correlated with FBI. Because both LSEI and FBI have been found to be beneficial to the e-learning experience, finding a positive relationship between the two is useful because a case can then be made that video lectures need to engage students in a way to increase their LSEI so they will continue to use video lectures in the future. A significant amount of research has tied satisfaction to FBI. Lee (2009) measured levels of perceived enjoyment and satisfaction with student intention to use e-learning and found that satisfaction had the most significant effect on learners’ intention to continue e-learning in the future. Roca et al. (2006), using the TAM model to look at reasons for student continuance, found that their intention to continue was determined by satisfaction. Alraimi et al. (2015) showed that continuing to use an e-learning platform was influenced by a number of factors, including student satisfaction. One approach to explore relationships that affect intention to use involves the use of flow theory. The flow theory is reflective of aspects of LSEI in a variety of ways. Students experience flow when their levels of involvement reach a point where other things happening around them appear insignificant and their sense of time becomes distorted (Hoffman and Novak, 2009). Flow is linked to student engagement, as students who experience flow show an increase in enjoyment and concentration (Lee, 2009). Researchers have tied engagement and interest to the flow theory by developing constructs that looked at perceived levels of the two

(Huang, 2003; Koufaris, 2002; Lee, 2009). Adding support for aspects of LSEI having a positive relationship with intention to use, Lee (2009) and Lee et al. (2009) found that there is a greater chance of FBI for students who experience a state of flow in e-learning. Furthermore, in Lee et al.’s (2009) study, students who believed e-learning improved their learning also showed higher levels of intention to use. Based on the findings of previous research, the final hypothesis of this study is as follows:


H4. LSEI is positively correlated with FBI. 4. Methods 4.1 Contextual background This research analyzed surveys completed by Open Cyber University (OCU) students in South Korea. The surveys reflect OCU classes taken during the second semester of 2015. Founded in 1997 and initiated in the fall semester of 1998, the OCU provides a wide range of online courses for university students in South Korea (Jung and Rha, 2001). The OCU is operated and funded by a network of 23 traditional brick-and-mortar universities, referred to as the consortium. These 23 universities provide the individual content, design and instructors for the OCU courses (Jung and Rha, 2001). As of 2016, the OCU is the largest online university system in South Korea, providing more than 400 courses to approximately 120,000 students each year. (About OCU, 2017). 4.2 Sample collection and data analysis Initially, ten students at a national university in South Korea were contacted to be interviewed regarding their involvement in the OCU. Specifically, they were asked questions regarding the effectiveness of OCU courses, the quality of the lectures and the general nature of each OCU lesson. Based on their responses, it was found that the OCU operated in a reasonably traditional manner, with little to no learner-to-learner interaction or learner-to-instructor interaction. For this reason, a broad survey of OCU focusing on student perceptions of the video lectures was felt to be the best method of understanding the most important aspect of OCU instruction, which is the video lecture. In January 2016, 105 students who had taken an OCU class in the fall semester were contacted and sent surveys to complete. Of those 105 students, 92 attempted the survey. However, among the 92 attempted surveys, four were incomplete to a point that precluded them from being able to be used for analysis. Those surveys were removed, leaving 88 participants in the study. Of those 88 participants, 26 were males and 62 were females. There were 25 seniors, 22 juniors, 26 sophomores and 15 freshmen. The average age of the subjects was 22 years, with the oldest being 29 years and the youngest being 19 years. All participants were full-time, brick-and-mortar students who had taken a variety of classes at the OCU to supplement their offline classes. 4.3 Instrument development The first phase of developing the instrument used in this survey was to conduct a series of qualitative interviews with students who had been part of the OCU. Initially, questions focused on learner-to-learner interaction, lecture style and viewership impacted student perceptions of the their cyber university class. However, it was found that none of the respondents had any learner-to-learner interaction. Therefore, a decision was made to focus on how the amount of lectures watched and the diversity of the lectures affected learner perceptions. There were 12 initial items in the survey that relate to this study, with one asking how many lectures the learners watched, five asking about lecture variety, three asking about


19

ITSE 14,1


20

learning, satisfaction, engagement and interest and three asking about the learner’s future intention derived from their cyber classes. A preliminary survey was conducted, and the results were discussed with ten participants regarding the appropriateness of the items. The item asking about how many lectures the learners watched was initially based around percentages: “What percentage of lessons did you watch?”. However, participants in the preliminary survey felt that phrasing the question in such a way was somewhat confusing. For that reason, it was changed to simply state, “How many of the lectures did you watch?”. There were six possible responses to the item: none, very few, less than half, about half, most or all. For the purposes of analysis, those categories were converted into a scale between 0 and 5, with 0 reflecting the response “none” and 5 representing the response “all”. Among all of the surveys that were used in this study, none of them contained the response “none”. Therefore, the final scale was between 1 and 5. This scale is similar to that used by Le et al. (2010) in which the amount of lectures watched during a semester was coded into six separate categories, including “none”. The categories for calculating media diversity were initially similar to Gou et al.’s (2016) six categories. However, according to participant responses during the qualitative survey creation phase, those categories required some modification. When discussing the way lectures were delivered, participants mentioned five ways that information was conveyed during lectures. Furthermore, several lectures were watched by the researchers. The methods of delivery observed were turned into items that could be checked after the question, “How were the lectures delivered?”. The five methods of delivery were as follows: slides with text, images, graphs, etc., slides with voice of professor, video of professor talking, video of professor talking with text in the background and other audio or visual multimedia (such as music, the recording of a speech or a video of something related to the contents of the lecture). There were three items that made up the LSEI scale. These items, or items in similar forms, have been used in a great deal of online research looking into students’ sense of learning (Arbaugh, 2000; Costley and Lange, 2016; Eom et al., 2006; Kim et al., 2011; Lee, 2010; Lee and Rha, 2009; Liaw, 2008; Lyons et al. 2012; Richardson and Swan, 2003; Shea et al., 2003; Sun et al., 2008; Thurmond et al., 2002). The item that was used to generate the students’ perception of learning was, “Overall I learned a lot in this class”. This item was adapted from Kim et al.’s (2011) perceived learning item (I think I learned a lot from this course), which was used to determine students’ perception of their e-learning experience. The item used to generate the students’ perception of satisfaction was, “Overall I was satisfied with this class”. This item was adapted from Arbaugh’s (2000) satisfaction item (I was very satisfied with this course), which was used to measure student satisfaction levels of an online course. The item used for creating the engagement and interest part of the learners’ perception was “Overall this class kept me engaged and interested”. This item was adapted from Sun and Rueda’s (2012) item (I am interested in the work at the online class), which measured interest of an online learning class as part of their engagement scale. Cronbach’s alpha was calculated for the three items (0.812) and was considered acceptable to combine them into one construct. The construct was eventually used to examine its relationship with the independent variables used in this study According to TAM, attitudes toward learning play a decisive role in learners’ intention to use (Davis et al., 1989). These attitudes include one’s feelings toward the topics being learned, to the instructors delivering the content and to the technology itself (Liaw et al., 2007). Reflecting the notion that these types of attitudes influence intention to use, and based on the original TAM model that represents behavioral intention to not only use the technology but to intend to use it along with features associated with it to aid learning, the following items were self-developed for this study: “This class made me want to learn more about the topic”,


“I would take a class with the same professor” and “I will not take another OCU class again”. The scale was designed to reflect the behavior that learners claimed would be generated from the class that they had taken. Items similar to these have been used in research (Alraimi et al., 2015; Chang et al., 2015; Lee, 2009; Liaw, 2008; Liaw et al., 2007; Roca et al., 2006). Cronbach’s alpha was calculated at 0.832, so the items were combined together generating a single construct, which was used to examine its relationship with the independent variables used in this study.


21

5. Results A description of the results from each variable is shown in Table I. The mean for amount of lectures watched was 3.05, whereas the diversity of lecture delivery was 2.09. These scales were out of 5. The variable for amount of lectures watched represents a quantification of student responses. A summary of this can be seen in Table II. The low amount of lectures watched by the students is of note, with only 45 per cent of the subjects watching “most” or “all” of the lectures. Although both dependent variable scores were low, LSEI (5.72) was higher than FBI (5.12). Both of these scales were out of 10. Not all measured variables were correlated (see Table III). The strongest statistically significant correlation was between FBI and LSEI (0.746) (p ⬍ 0.01), followed by the correlation between the amount of lectures watched and LSEI (0.247) (p ⬍ 0.05), and diversity of lecture delivery and LSEI (0.235) (p ⬍ 0.05) representing the least significant correlation. The amount of lectures watched (0.138) and diversity of lecture delivery (0.098) were not statistically significantly correlated with FBI. Furthermore, there was not a statistically

Variables Amount of lectures watched Diversity of lecture delivery LSEI FBI

All Most About half Less than half Very few None

No. of items

Minimum

Maximum

Mean

Standard deviation

1 5 3 3

1 1 1.33 1.33

5 5 10 10

3.05 2.09 5.72 5.12

1.59 1.18 1.69 1.60

Frequency

(%)

Cumulative (%)

26 12 12 16 22 0

29.5 13.6 13.6 18.2 25 0

29.5 43.1 56.7 74.9 100 100

Amount of lectures

Diversity

LSEI

1 ⫺01 0.247* 0.138

1 0.235* 0.098

1 0.746**

Amount of lectures Diversity LSEI FBI Notes: * p ⬍ 0.05; ** p ⬍ 0.01

Table I. Mean and standard deviation of variables (n ⫽ 88)

Table II. Amount of lectures watched (n ⫽ 88)

FBI

1

Table III. Correlations between amount of lectures watched, diversity, LSEI and FBI (n ⫽ 88)

ITSE 14,1


22

significant correlation between amount of lectures watched and diversity of lecture delivery (⫺0.01). Demographic variables were examined to examine their relationships with amount of lectures watched, diversity of lecture delivery, LSEI and FBI. Moreover, t-tests of the differences between genders showed no statistically significant comparisons (see Appendix 1). Also, an ANOVA test was performed that showed no statistically significant relationship between student grade levels and any of the variables (see Appendix 2). Furthermore, correlations were calculated between subject age and the variables, with none of the relationships being statistically significant (see Appendix 3). Multiple regression analysis was used to find out the degree to which the amount of lectures watched and the diversity of lecture delivery affected both LSEI and FBI. As seen in Table III, the combination of the independent variables accounted for a small (12 per cent), but statistically significant, percentage of the variance. The whole model was shown to be statistically significant with a p-value of 0.005 (F ⫽ 5.656). Multiple regression was again used to assess if the combined effects of amount of lectures watched and diversity of lecture delivery had a greater effect on FBI than they had individually (see Table IV). The results show an R2 value that is not statistically significant (R2 ⫽ 0.029, p ⫽ 0.285). 6. Discussion This study’s main focus was to investigate whether media diversity and lecture viewership have an effect on learning, represented by the LSEI construct. Because satisfaction, engagement and learning have all been shown to be predictors of a positive learning experience (Astin, 1993; Bailey et al., 1998; Chute et al., 1999; Edwards and Waters, 1982; Donohue and Wong, 1997; Guo et al., 2014), and the fact that they were found to be similar in regards to learning in this study, they were added to perceived learning to form a single construct. However, not all aspects of e-learning that may engage, raise interest or satisfy students necessarily lead to learning. But in a general sense, it has been shown that when these factors are applied to goal-related activities, students generally learn what is expected of them (Huang, 2003; Koufaris, 2002; Lee, 2009; Sun et al., 2008). It is important to note that any intervention should be implemented in a way that leads to learning so that the likelihood of students’ intent to continue with such learning in the future increases. Based on this notion, a secondary investigation was performed in this study to see if diverse media delivery also leads to higher levels of lecture viewership and continued usage of the e-learning system. This is important because if media diversity leads to increased levels of LSEI, it would be advantageous for students to continue participating in such environments to promote learning in the future. Based on this notion, this study examined the hypotheses to see if

LSEI

Table IV. Multiple regression tables for LSEI and FBI

Amount of lectures watched Diversity of lecture delivery F Degrees of freedom R R2 Adjusted R2

FBI

␤

t

␤

t

0.247 0.235 5.656*** 85 0.343 0.117 0.097

2.445* 2.330*

0.138 0.098 1.273 85 0.171 0.029 0.006

1.303 0.932

Notes: * p ⬍ 0.05; ** p ⬍ 0.01; *** p ⬍ 0.001


media diversity not only leads to learning but if such potential associations with learning also lead to continual usage in the future. The results did not support H1, which predicted that the delivery of diverse forms of media would be positively correlated with the amount of lectures watched. Currently, there is not much research focused on this area, but the research that does exist suggests that including various forms of media along with a visual of the professor should lead to higher levels of viewership (Guo et al., 2014). However, the findings of this study found no relationship between lecture diversity and viewership. In this study, it is apparent that although diverse media integration may have been viewed as helpful to the students, it was not a determining factor in watching more videos. It may be possible that the additional material associated with the diverse delivery may have restricted viewership because of the extended time required to view such material. Furthermore, if media diversity required more effort to make connections with the specified goals, viewership may have also been restricted. For those with high levels of viewership, there may have been other motivating factors that led them to make their decision to watch more videos, but it is apparent that the presentation style did not influence their decision. One such factor may be that attendance in the OCU is represented by actual video lecture viewership. This may have led students to watch more videos to gain a better attendance grade, regardless of the media diversity included in the videos. H2a was supported by the results of this study, in that media diversity was positively correlated with LSEI. This is consistent with what most research shows in regards to the relationship between diverse delivery and factors associated with LSEI. Kim et al.’s (2011) study provides support for what the current study defines as media diversity. They describe a variety of media integration using similar media formats used in this study to describe lecture diversity, and they found that such variety is related to higher levels of learning and satisfaction. Similar research shows that information presented with diverse forms of media, rather than a straight lecture, is helpful for students not only to get a better understanding of the information but also in holding their interest (Klass, 2003; Rawat et al., 2014). Other research has found relationships between a combination of multimedia and higher levels of engagement (Michelich, 2002; Zhang et al., 2006). Based on this study and prior research that support its findings, the suggestion to e-learning instructors is fairly simple and straightforward: integrate lecture delivery with diverse forms of media to not only keep student interest and help them understand the topic but to ultimately increase all aspects of LSEI. The findings of the present study strengthen the argument that media diversity in an e-learning environment is beneficial to the learning process by relating it not only to selective aspects of LSEI but relating it also to the combined construct of LSEI. Instructors need to be multifaceted in their use of media in presenting their lectures because doing so should produce more positive outcomes represented by LSEI. Although it was found that there was a relationship between lecture diversity and LSEI, H2b was rejected in that media diversity did not appear to have an effect on students’ FBI to continue with online learning at the cyber university. Other research has found an indirect relationship between lecture diversity and intention to use, through mediation with perceived usefulness (Liaw, 2008; Liu et al., 2009). However, the current study did not use mediation to link diverse lecture delivery to FBI. It is therefore apparent that although lecture diversity affected LSEI, it did not have a direct influence on students’ decisions to continue studying in the future. It may be the case that specific aspects associated with the media diversity, which lead to learning, did not lead to intention to use in the future. This may include the additional time and effort associated with additional content presented through diverse media. Although such effort can lead to enhanced levels of learning, it may have been


23

ITSE 14,1


24

too much for students to commit to such instruction in the future. For those students that showed high levels of FBI, other factors apparently influenced their decisions on whether to continue to use or not. Unfortunately, looking into such factors is beyond the scope of this study. Future research needs to examine other factors that lead to students’ intention to use, in addition to finding specific ways of sufficiently promoting student continuance by diversifying lecture delivery. The results of this study supported H3a, in that the amount of lectures watched was positively correlated with LSEI. The relationship between viewership and LSEI generally supports the notion that watching videos leads to higher levels of learning, satisfaction, engagement and interest (Bongey, 2006; Chandra, 2007; Danielson et al., 2014; Owston et al., 2011; Toppin, 2011; Traphagan, 2005; Traphagan et al., 2010). The current study focused specifically on the frequency of viewership, rather than the general usage of the videos. The resulting outcome was telling, in that students who watch more videos tend to perceive higher levels of LSEI. Although the results from other studies generally support the notion that watching videos is linked to specific aspects of LSEI, this study found that watching more videos is related to LSEI as a combined construct. Thus, the current study shows that as viewership of video lectures increases, the more learning is supported through higher levels of LSEI. From these results, the conclusion can be made that e-learning instructors need to produce video lectures in a way that encourages higher levels of viewership among the learners. The educational benefits to watching more videos are apparent, and instructors need to make sure they are engaging their students in a way that would promote higher viewership levels. According to the findings of this research, the positive effect that watching more videos has on LESI does not carry over to student intentions to continue using the e-learning system (H3b). Past research has looked to see if a relationship exists between experience of watching video lectures and students’ future intention to use e-learning. Results from such research are mixed in this regard (Giannakos and Vlamos, 2013; Giannakos et al., 2015). Both the Giannakos and Vlamos (2013) study and the Giannakos et al. (2015) study hypothesized that because of research conducted by Liao and Lu (2008) showing that experience in Web-based learning has an effect on intention to use, the same principles would be applied for video lectures. However, Giannakos et al. (2015) could not support the hypothesis with their findings. Like Giannakos et al. (2015), this research was unable to substantiate the claim that having more experience in viewing lectures leads to higher FBI. One reason may be that if the videos were perceived to be too time-consuming, students may have been turned off to the idea of going through the same process in the future. This is supported by Guo et al. (2014), who claim that video lectures should be limited in length to ensure optimal levels of engagement. Because minimal research exists on this area, more analysis needs to be done to see how to promote higher levels of FBI through higher levels of video viewership. Perhaps, there may be other factors that when combined with high levels of video usage leads to higher levels of FBI. However, this study looked at the direct relationship between viewing levels and FBI, and the results of this study show that watching more videos does not lead students to be more likely to continue using e-learning. Finally, the results of this study support H4, which predicted that LSEI would be positively correlated with FBI. Research supports this by tying aspects of LSEI to student intention to use e-learning (Alrami et al., 2014; Lee, 2009; Roca et al., 2006). Most of the research in this regards shows a relationship between satisfaction and FBI, as it appears that students who are more satisfied with their e-learning experience are more prone to continue using e-learning. Engagement and interest has also been linked to FBI through the use of the flow theory (Huang, 2003; Koufaris, 2002; Lee, 2009). Although other studies were successful


in demonstrating the importance of showing that specific aspects of LSEI affect levels of FBI, this study confirmed that LSEI as a combined construct has a relationship with FBI. 7. Conclusion and limitations Using survey analysis of students who participated in cyber university courses in Korea, this study found that the amount of lectures viewed and media diversity both positively affected LSEI. However, FBI was found to have no relationship with either the amount of lectures viewed or lecture diversity. Additionally, media diversity did not lead to more viewership of the video lectures. Finally, LSEI was found to have a relationship with FBI. Given the positive relationships found with LSEI in this study, it can be concluded that media diversity and lecture viewership are useful for the online learning process. However, unexpected results were found, specifically that media diversity and watching more videos did not affect FBI. Because media diversity and watching more videos have been shown to have an effect on LSEI, it was assumed that they would positively influence FBI as well. However, the results show that media diversity and high levels of viewership did not influence the behavioral intention to continue the use of cyber university courses in the future. Through its findings, this study serves as an important starting point for using media diversity to promote learning, represented in this study by combining learning, satisfaction, engagement and interest into a single learning construct. Using these findings as a first step, future research needs to expand on the results in a way that will further enhance the learning experience. The current study looks at media diversity in a general sense, and it is beyond its scope to examine specific conditions under which it would have the most impact. One way to add to the positive relationships found with media diversity is to set experimental conditions to see exactly how and when media diversity should be applied within an e-learning course. Future research needs to look deeper into why media diversity and lecture viewership is not linked with FBI, given the fact that they were shown to be linked with learning. Follow-up interviews of participants may provide useful information as to why no relationships were found with those variables. Additionally, looking into specific reasons as to why the students watch or do not watch videos would be useful for future recommendations of how to improve video lectures in a way that best engages students. For example, the fact that this study did not find a link between media diversity and amount of videos watched could be explained further through qualitative questioning. Using that approach may be useful to find out if the videos were consistent with their media diversity throughout the course. For example, some instructors may use diverse media delivery, but be inconsistent in its application at the beginning of the course. This may have had a negative effect of viewership early on, only to have the students discover that when they check back in later in the semester, the lectures suddenly contained diverse media integration that was not there in the first few lessons.

Lecture diversity

LSEI

Amount of lectures

FBI


25

Figure 1. The research model

ITSE 14,1


26

More detailed research could uncover factors of this type. It should also be noted that the limited sample size used in this study is too small to make generalizations to the whole population of OCU users. Although there are some limitations to this study, it does provide useful information for e-learning instructors who wish to enhance the learning experience through the delivery of diverse forms of media. References About OCU (2017). “In Open Cyber University”, available at: www.ocu.ac.kr/foreign/english/About_ ocu/sub05.asp (accessed 17 February 2016). Al-Qahtani, A.A. and Higgins, S.E. (2013), “Effects of traditional, blended and e-learning on students’ achievement in higher education”, Journal of Computer Assisted Learning, Vol. 29 No. 3, pp. 220-234. Alraimi, K.M., Zo, H. and Ciganek, A.P. (2015), “Understanding the MOOCs continuance: the role of openness and reputation”, Computers & Education, Vol. 80, pp. 28-38. Arbaugh, J.B. (2000), “Virtual classroom characteristics and student satisfaction with internet-based MBA courses”, Journal of Management Education, Vol. 24 No. 1, pp. 32-54. Astin, A.W. (1993), What Matters in College? Four Critical Years Revisited, Jossey Bass, San Francisco, CA. Bailey, B.L., Bauman, C. and Lata, K.A. (1998), “Student retention and satisfaction: the evolution of a predictive model”, paper presented at the Association for Institutional Research Conference, Minneapolis, MN. Bouhnik, D. and Marcus, T. (2006), “Interaction in distance-learning courses”, Journal of the American Society Information Science and Technology, Vol. 57 No. 3, pp. 299-305. Brecht, H.D. (2012), “Learning from online video lectures”, Journal of Information Technology Education, Vol. 11 No. 1, pp. 227-250. Brecht, H.D. and Ogilby, S.M. (2008), “Enabling a comprehensive teaching strategy: video lectures”, Journal of Information Technology Education, Vol. 7, pp. 71-86. Chandra, S. (2007), “Lecture video capture for the masses”, ACM SIGCSE Bulletin, Vol. 39 No. 3, pp. 276-280. Chang, C.C., Liang, C., Shu, K.M. and Chiu, Y.C. (2015), “Alteration of influencing factors of e-learning continued intention for different degrees of online participation”, International Review of Research in Open and Distance Learning, Vol. 16 No. 4, pp. 33-61. Chute, A.G., Thompson, M.M. and Hancock, B.W. (1999), The McGraw-Hill Handbook of Distance Learning, McGraw-Hill, New York, NY. Cole, M.T., Shelley, D.J. and Swartz, L.B. (2014), “Online instruction, e-learning, and student satisfaction: a three year study”, The International Review of Research in Open and Distributed Learning, Vol. 15 No. 6, pp. 111-131. Costley, J. and Lange, C. (2016), “The effects of instructor control of online learning environments on satisfaction and perceived learning”, The Electronic Journal of e-Learning, Vol. 14 No. 3, pp. 169-170. Danielson, J., Preast, V., Bender, H. and Hassall, L. (2014), “Is the effectiveness of lecture capture related to teaching approach or content type?”, Computers and Education, Vol. 72, pp. 121-131, available at: http://doi.org/10.1016/j.compedu.2013.10.016 Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol. 35 No. 8, pp. 982-1003. Edwards, J.E. and Waters, L.K. (1982), “Involvement, ability, performance, and satisfaction as predictors of college attrition”, Educational and Psychological Measurement, Vol. 42 No. 4, pp. 1149-1152.


Eom, S.B., Wen, H.J. and Ashill, N. (2006), “The determinants of students’ perceived learning outcomes and satisfaction in university online education: an empirical investigation”, Decision Sciences Journal of Innovative Diversity of Lecture Delivery is Positively Correlated with Amount of the Lectures Watched. Education, Vol. 4 No. 2, pp. 215-235, available at: http://doi.org/10.1111/j. 1540-4609.2006.00114.x Giannakos, M., Jaccheri, M.L. and Krogstie, J. (2015), “Exploring the relationship between video lecture usage patterns and students’ attitudes”, British Journal of Educational Technology, Vol. 47 No. 6, pp. 1259-1275, doi: 10.1111/bjet.12313. Giannakos, M.N. and Vlamos, P. (2013), “Educational webcasts’ acceptance: empirical examination and the role of experience”, British Journal of Educational Technology, Vol. 44 No. 1, pp. 125-143. Grandon, E.E., Alshare, K. and Kwun, O. (2005), “Factors influencing student intention to adopt online classes: a cross-cultural study”, Journal of Computing Sciences in Colleges, Vol. 20 No. 4, pp. 46-56. Guo, P.J., Kim, J. and Rubin, R. (2014), “How video production affects student engagement : an empirical study of MOOC videos”, Proceedings of the 1st ACM Conference on Learning at Scale, pp. 41-50, available at: http://doi.org/10.1145/2556325.2566239 Hoffman, D.L. and Novak, T.P. (2009), “Flow online: lessons learned and future prospects”, Journal of Interactive Marketing, Vol. 23 No. 1, pp. 23-34. Huang, M. (2003), “Designing website attributes to induce experiential encounters”, Computers in Human Behavior, Vol. 19 No. 4, pp. 425-442. Jung, I. (2000), “Korea: virtual university trial project”, TechKnowLogia, Vol. 2 No. 1, pp. 29-31. Jung, I. and Rha, I. (2001), “A virtual university trial project: its impact on higher education in South Korea”, Innovations in Education and Teaching International, Vol. 38 No. 1, pp. 31-41. Kalyuga, S., Chandler, P. and Sweller, J. (1999), “Managing split-attention and redundancy in multimedia instruction”, Applied Cognitive Psychology, Vol. 13 No. 4, pp. 351-371. Kim, J., Guo, P.J., Seaton, D.T., Mitros, P., Gajos, K.Z. and Miller, R.C. (2014), “Understanding in-video dropouts and interaction peaks in online lecture videos”, in Proceedings of the First ACM Conference on Learning at Scale Conference, ACM, New York, NY, pp. 31-40, available at: http://doi.org/10.1145/2556325.2566239 Kim, J., Kwon, Y. and Cho, D. (2011), “Investigating factors that influence social presence and learning outcomes in distance higher education”, Computers & Education, Vol. 57 No. 2, pp. 1512-1520, available at: http://doi.org/10.1016/j.compedu.2011.02.005 Klass, B. (2003), “Streaming media in higher education: possibilities and pitfalls.”Campus Technology. Koufaris, M. (2002), “Applying the technology acceptance model and flow theory to online consumer behavior”, Information Systems Research, Vol. 13 No. 2, pp. 205-223. Le, A., Joordens, S., Chrysostomou, S. and Grinnell, R. (2010), “Online lecture accessibility and its influence on performance in skills-based courses”, Computers & Education, Vol. 55 No. 1, pp. 313-319. Lee, H. and Lee, S. (2015), “Analysis of various influences and factors on academic persistence of cyber university students”, International Journal of Service, Science and Technology, Vol. 8 No. 10, pp. 211-222. Lee, H.J. and Rha, I. (2009), “Influence of structure and interaction on student achievement and satisfaction in web-based distance learning”, Educational Technology and Society, Vol. 12 No. 4, pp. 372-382. Lee, M.C. (2010), “Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation-confirmation model”, Computers and Education, Vol. 54 No. 2, pp. 506-516, available at: http://doi.org/10.1016/j.compedu.2009.09.002 Levy, Y. (2007), “Comparing dropouts and persistence in e-learning courses”, Computers and Education, Vol. 48 No. 2, pp. 185-204, available at: http://doi.org/10.1016/j.compedu.2004.12.004


27

ITSE 14,1


28

Liaw, S.S. (2008), “Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: a case study of the blackboard system”, Computers and Education, Vol. 51 No. 2, pp. 864-873, available at: http://doi.org/10.1016/j.compedu.2007.09.005. Liaw, S.S., Huang, H.M. and Chen, G.D. (2007), “Surveying instructor and learner attitudes toward e-learning”, Computers & Education, Vol. 49 No. 4, pp. 1066-1080. Liu, S.H., Liao, H.L. and Pratt, J.A. (2009), “Impact of media richness and flow on e-learning technology acceptance”, Computers & Education, Vol. 52 No. 3, pp. 599-607. Lowe, R.K. (1999), “Extracting information from an animation during complex visual learning”, European Journal of Psychology of Education, Vol. 14 No. 2, pp. 225-244. Lyons, A., Reysen, S. and Pierce, L. (2012), “Video lecture format, student technological efficacy, and social presence in online courses”, Computers in Human Behavior, Vol. 28 No. 1, pp. 181-186, available at: http://doi.org/10.1016/j.chb.2011.08.025. Mayer, R.E. (2014), “Introduction to Multimedia Learning”, in Mayer, R.E. (Ed.),The Cambridge Handbook of Multimedia Learning, Cambridge University Press, Cambridge, pp. 1-24. Mayer, R.E. and Moreno, R. (2003), “Nine ways to reduce cognitive load in multimedia learning”, Educational Psychologist, Vol. 38 No. 1, pp. 43-52. Michelich, V. (2002), “Streaming media to enhance teaching and improve learning”, The Technology Source. National Center for Education Statistics. (2008), Distance Education at Degree-Granting Postsecondary Institutions: 2006 – 07, US Department of Education, NCES, pp. 2009 –2044. Oomen-Early, J., Bold, M., Gallien, T.L., Wiginton, K.L. and Anderson, N. (2008), “Using asynchronous audio communication (AAC) in the online classroom: a comparative study”, MERLOT Journal of Online Learning and Teaching, Vol. 4 No. 3, pp. 267-276. Owston, R., Lupshenyuk, D. and Wideman, H. (2011), “Lecture capture in large undergraduate classes: student perceptions and academic performance”, Internet and Higher Education, Vol. 14 No. 4, pp. 262-268, available at: http://doi.org/10.1016/j.iheduc.2011.05.006 Rasch, T. and Schnotz, W. (2009), “Interactive and non-interactive pictures in multimedia learning environments: effects on learning outcomes and learning efficiency”, Learning and Instruction, Vol. 19 No. 5, pp. 411-422. Rawat, Y.S., Bhatt, C. and Kankanhalli, M.S. (2014), “Mode of teaching based segmentation and annotation of video lectures”, In Proceedings – International Workshop on Content-Based Multimedia Indexing, available at: http://doi.org/10.1109/CBMI.2014.6849840 Richardson, J. and Swan, K. (2003), “Examination of social presence in online learning: students’ perceived learning and satisfaction”, Journal of Asynchronous Learning Networks, Vol. 7 No. 1, pp. 68-88. Roca, J.C., Chiu, C.M. and Martínez, F.J. (2006), “Understanding e-learning continuance intention: an extension of the technology acceptance model”, International Journal of human-computer studies, Vol. 64 No. 8, pp. 683-696. Russo, T. and Benson, S. (2005), “Learning with invisible others: perceptions of online presence and their relationship to cognitive and affective learning”, Educational Technology and Society, Vol. 8 No. 1, pp. 54-62, available at: http://doi.org/ica_proceeding_11822.PDF Schnotz, W. and Rasch, T. (2005), “Enabling, facilitating, and inhibiting effects of animations in multimedia learning: why reduction of cognitive load can have negative results on learning”, Educational Technology Research and Development, Vol. 53 No. 3, pp. 47-58. Shea, P., Fredericksen, E., Pickett, A. and Pelz, W. (2003), “A preliminary investigation of teaching presence in the SUNY Learning Network”, Elements of Quality Online Education: Practice and Direction, Vol. 4, pp. 279-312. Sims, V.K. and Hegarty, M. (1997), “Mental animation in the visuospatial sketchpad: evidence from dual-task studies”, Memory & Cognition, Vol. 25 No. 3, pp. 321-332.


Sun, J.C.Y. and Rueda, R. (2012), “Situational interest, computer self-efficacy and self-regulation: their impact on student engagement in distance education”, British Journal of Educational Technology, Vol. 43 No. 2, pp. 191-204. Sun, P.C., Tsai, R.J., Finger, G., Chen, Y.Y. and Yeh, D. (2008), “What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction”, Computers and Education, Vol. 50 No. 4, pp. 1183-1202, available at: http://doi. org/10.1016/j.compedu.2006.11.007 Swan, K. (2001), “Virtual interaction: design factors affecting student satisfaction and perceived learning in asynchronous online courses”, Distance Education, Vol. 22 No. 2, pp. 306-33l. Sweller, J. (1999), Instructional Design in Technical Areas, ACER Press, Camberwell. Sweller, J., Van Merriënboer, J.J.G. and Paas, F. (1998), “Cognitive architecture and instructional design”, Educational Psychology Review, Vol. 10 No. 3, pp. 251-296. Thurmond, V.A., Wambach, K. and Connors, H.R. (2002), “Evaluation of student satisfaction: determining the impact of a web-based environment by controlling for student characteristics”, The American Journal of Distance Education, Vol. 16 No. 3, pp. 169-189. Toppin, I.N. (2011), “Video lecture capture (VLC) system: a comparison of student versus faculty perceptions”, Education and Information Technologies, Vol. 16 No. 4, pp. 383-393, available at: http://doi.org/10.1007/s10639-010-9140-x Traphagan, T. (2005), “Class lecture Webcasting, Fall 2004 and Spring 2005: a case study”, University of Texas-Austin Working Paper. Traphagan, T., Kucsera, J.V. and Kishi, K. (2010), “Impact of class lecture webcasting on attendance and learning”, Educational Technology Research and Development, Vol. 58 No. 1, pp. 19-37, available at: http://doi.org/10.1007/s11423-009-9128-7 van Merriënboer, J.J.G. (1997), Training Complex Cognitive Skills, Educational Technology Publications, Englewood Cliffs, NJ. Venkatesh, V. and Davis, F.D. (2000), “A theoretical extension of the technology acceptance model: four longitudinal field studies”, Management Science, Vol. 46 No. 2, pp. 186-204. Zhan, Z. and Mei, H. (2013), “Academic self-concept and social presence in face-to-face and online learning: perceptions and effects on students’ learning achievement and satisfaction across environments”, Computers & Education, Vol. 69, pp. 131-138. Zhang, D., Zhao, J.L., Zhou, L. and Nunamaker, J.F. (2006), “Can e-learning replace classroom learning?”, Communications of the ACM, Vol. 47 No. 5, pp. 75-79, available at: http://doi.org/10.1145/986213.986 216


29

ITSE 14,1

Appendix 1

Variable

30


Table AI. T-test for gender (n ⫽ 88)

Amount of lectures Diversity LSEI FBI

Gender

N

Mean

Standard deviation

p

Male Female Male Female Male Female Male Female

26 62 26 62 26 62 26 62

2.92 3.10 1.88 2.18 5.81 5.68 5.26 5.06

1.74 1.53 1.28 1.34 1.69 1.70 1.75 1.55

0.643 0.291 0.748 0.573

Appendix 2

Variable Table AII. Summary of ANOVA analysis for grade (n ⫽ 88)

Amount of lectures Diversity LSEI FBI

F

p

1.128 0.668 1.426 1.223

0.349 0.616 0.212 0.307

Appendix 3 Table AIII. Summary of correlations between subject age and variables (n ⫽ 88)

Pearson correlation Significance (two-tailed)

Lectures watched

Diversity

LSEI

FBI

0.017 0.874

⫺0.010 0.924

⫺0.119 0.271

⫺0.184 0.085

About the authors Jamie Costley is a Visiting Professor in the Department of English Education at Kongju National University in South Korea, where he also earned his PhD degree in Instructional Design. He has been involved in teaching students in blended learning situations and researching effective online instructional strategies since 2010. His main area of research is the impact of task or learning environment design on student-to-student interaction. He is currently involved in research into improving instruction in online classes in South Korea, and welcomes contact on this topic. Christopher Henry Lange is a Visiting Professor in the Liberal Arts Department at Joongbu University in South Korea. He has collaboratively published papers on group work and e-learning environments. His current research interests are effects of interaction within online learning environments. Furthermore, he is interested in investigating ways of improving online instruction, design and delivery to better address the needs of e-learning students. He has a Master’s of Education degree from Kongju National University in South Korea, and is currently enrolled in the PhD program there. Christopher Henry Lange is the corresponding author and can be contacted at: [email protected]

For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected]