Abstract
Technological shifts in instructional delivery have increased the number of online courses and programs available in higher education. In the design fields, many traditional lecture-based courses have been converted to online formats in an effort to bolster student enrollment numbers, increase weighted student credit hours, and help with recruitment. A panel discussion on the topic at the 2018 annual meeting of the Council of Educators in Landscape Architecture (CELA) identified four issues expected to affect the likelihood of success, including access, interactivity, online preferences, and concerns over academic integrity. Using these factors as an analytical framework, this article examines the current assets and capacities for online-learning delivery in a university-level lecture course related to the history of landscape architecture. Using three years of course evaluation data, students entering a high-enrollment online History of Landscape Architecture course are compared with similar survey data gathered after more students completed the course. Findings suggest that although online learning can increase the flexibility of course scheduling, boost the self-motivation of students, and remove geographic barriers for instructional delivery, technical challenges and the limited amount of student–teacher and student–student interaction may limit its capabilities in landscape architecture.
INTRODUCTION
The institutional shift toward promoting online instruction in higher education generated a reevaluation of the methods of instructional delivery in landscape architecture (LA) education. Trends in funding and administrative priorities produced a substantial growth in online course offerings in higher education (Christensen & Eyring 2011; Lokken & Mullins 2014). In reaction to this shift, a panel discussion at the 2018 Council of Educators in Landscape Architecture (CELA) conference examined the feasibility of using exclusively online learning for delivering an LA degree courses and ultimately entire programs (Pritchard et al. 2018). Panel members and an audience of instructors, administrators, practitioners, and researchers discussed an array of concerns related to online institutional delivery of courses in LA.
During the discussion, four issues emerged that significantly affect the likelihood of success for fully online LA courses (as well as programs): access, interactivity (including collaboration and engagement), online preferences, and concerns over academic integrity. The panel identified access as referring to both the awareness and admittance of students into the LA field. Concerns focused on declining LA enrollment and the need to increase enrollments (Pritchard & Martinez 2017). This condition is broader than the LA field. Nationally, U.S. higher education is seeing declining enrollments across most majors specific to the arts and allied design-related programs, schools, and departments (Haag 2015).
Online education can potentially enhance enrollment numbers by advertising the profession within a large clientele and reaching populations with no access to an LA program. A primary concern of this approach is the facilitation of student-instructor and student–student communication and interaction. The degree of instructor engagement necessary for teaching an effective LA course requires considerable interactivity among students and professors compared with many other disciplines (Schön 1985). Demonstrating online learning tools that will facilitate and foster increased communication in what is perceived to be limited online capacity is a primary hurdle to implementing effective online instructional delivery.
Many faculty are concerned that current online communication methods are insufficient for studio-based courses (George, Shelton, & Walker 2017). Questions on the ability to achieve the interactivity necessary to effectively teach these courses also concerned many attendees at the 2018 CELA panel. Nevertheless, preference for online instruction has increased significantly in the past decade, and the current generation of students seems favorably disposed toward online learning (Allen & Seaman 2016; Harasim 2000; Paechter & Maier 2010). The examination of mixed media delivery methods is a first step toward bridging the gap between interactivity needs and student preference for online learning. These approaches are already being used in blended, flipped, and traditional courses (Newman et al. 2016).
Two strong counterarguments to online learning in LA programs include concerns that students are able to cheat more easily and that online instruction may not have the capacity to cater to the broad range of course typologies needed for effectively teaching LA. Although the literature on online learning identifies multiple methods for limiting cheating, the ability to distill the distinctive needs of educating LA students, compared with other majors, has not been fully evaluated. Course typologies in LA range from studio (e.g., service-learning projects) or seminar (e.g., theory) courses to traditional lecture (e.g., landscape history) and hybrid lecture-studio courses (e.g., construction courses). The uniqueness of this broad range of typologies may create different needs for online-based instructional delivery in this field.
To address the issues discussed by the 2018 CELA panel and to identify the online learning needs that are specific to LA students, this article discusses the current climate of online education in LA and explores the assets and capacities for online learning and instruction. We report on statistical analyses from a survey of students enrolled in an online history of landscape architecture course over three years (eight semesters). Pre- and postcourse analyses are presented using access, interactivity, online preference, and academic integrity (cheating) as analytical measures. By examining students’ overall preference for online learning and the extent to which each measure is prevalent, we suggest approaches for delivering a successful online LA course.
LITERATURE REVIEW
There is a limited body of literature concerning online education in design fields, including LA. Current research has often produced contradictory findings (Bender, Wood, & Vredevoogd 2004; George, Shelton, & Walker 2017). In addition, the research focused largely on assessing the application of new learning technologies, as opposed to assessing the impact of online education on traditional lecture or studio pedagogy and other implications associated with transferring design-based courses to an online format (Brown & Cruickshank 2003). Results of this research are inconclusive (Bender 2005; Li 2007).
Early Interest in Design-Based Online Learning
Interest in online delivery of broader design-related education began in the mid-1990s, with research conducted at several universities, including the National University of Singapore, the University of Sydney, the University of British Columbia, Cornell University, and George Washington University (Broadfoot & Bennett 2003; Dale 2006; Maher, Bilda, & Gül 2006; Sagun, Demirkan, & Goktepe 2001). The early literature examined largely temporary interventions that focused on the potential of online education in design fields. In a continuing trend, these projects primarily examined the technology used in classrooms rather than their pedagogical implications (Bender & Good 2003; Budd, Vanka, & Runton 1999; Maher & Simoff 1999; Maher, Simoff, & Cicognani 1996; Simoff & Maher 1997). Early exceptions to this trend were Cheng’s (1998) assessment of online education on social relationships and Kvan’s (2001) recognition and discussion about the need for a new, online-centric design pedagogy.
Despite these constraints, research into online design education consistently reported promising results throughout the 1990s and the early 2000s. Researchers noted several positive aspects, including expanded spatial access by connecting geographically dispersed individuals (Dave & Danahy 2000; Kvan 2001; Levine & Wake 2000) and helping students break out of their sociocultural gestalt by exposing them to new social or cultural perspectives (Kvan 2001; Sagun, Demirkan, & Goktepe 2001). This research noted that online education provided reduced temporal access barriers by providing flexibility in the timing of using online systems for students and faculty (Kvan 2001; Li & Murphy 2004). Promising findings reported that online education may have the potential to improve a student’s focus and understanding of the design process because the medium inherently organizes and preserves iterations of data generation (Sagun, Demirkan, & Goktepe 2001).
Concerns about Use of Online Education
Concerns focused on the effectiveness of online education. This included a general disappointment in its failure to fully deliver on the promise of early studies to promote adoption of online education (Radclyffe-Thomas 2008). Specific concerns revolved around implementation, focusing on issues such as costs (time and money) to use new technologies and programs (Bender & Vredevoogd 2006; Li 2007; Radclyffe-Thomas 2008; Park 2011). Design faculty also have trepidations that the online learning experience was incompatible with the studio environment because it failed to reproduce the rich social learning setting of studio education (Broadfoot & Bennett 2003; Niculae 2011; Saghafi, Franz, & Crowther 2012a). When learning how to design, the most critical interaction is between the student and studio instructor (Schön 1985). Although it may appear easy to produce good design, as the learner is initiated, it becomes apparent that it is difficult to master the design process. The paradox of teaching design is that the student does not yet understand how to design, yet he or she can only gain this understanding by designing (Schön 1983). Thus, the design instructor plays a critical role in mentoring students through this experience. Some believe that online design education can fall significantly short in facilitating creation of this relationship (Niculae 2011; Saghafi, Franz, & Crowther 2012a).
Recent Holistic Approaches to Online Education in Design Fields
Researchers have recently taken a more systematic approach to non–studio-based design-related instruction, examining online design education in a holistic way instead of focusing heavily on technology. George (2018) reviewed a semester-long implementation of a graphics studio course and found the achievement of learning objectives between online and face-to-face students was similar. Saghafi, Franz, and Crowther (2012b) concluded that online design education gives the learner improved control over the learning experience. George, Shelton, and Walker (2017) surveyed the specific barriers discouraging faculty adoption of online education. Identifying issues with supporting rich social interaction resulted in applying social networking theories in online education. This work seeks to improve the social learning experience of the students by leveraging existing social connections and facilitating the creation of new connections. Notably, this has been attempted with the adoption of social networking applications (for example, Facebook) to encourage greater collaboration among online students (Ham & Schnable 2011; Wang 2011). This conclusion was supported by George’s (2017) findings that social media use enhances social interactions compared with discussion boards, which have been traditionally used in online courses.
Academic Integrity
Aside from academic performance, online education concerns focus on academic integrity. Worries about the prevalence of cheating in higher education are not new. It is a growing concern among educators and researchers, with recent studies showing that a majority of students participate in some form of cheating at least once in their academic career (King, Guyette, & Piotrowski 2009; Rozycki 2006). In addition, the number of students participating in cheating has steadily increased since the 1960s (Olt 2002; Rozycki 2006). Some perceive that the growing popularity of online education increased the prevalence of cheating among college students (King, Guyette, & Piotrowski 2009). In using digital tools in online education, students still participate in cheating activities despite being physically separated from each other (Olt 2002; Rogers 2006).
At the same time, the online climate allows students who cheat to do so from a distance. Identification of those who cheat in online courses requires of specialized tools (Rogers 2006). New group-based phone apps and web-based data sharing platforms may exacerbate the ease of information sharing, thereby increasing capabilities and methods for cheating. As a result, faculty and students typically believe it is easier to cheat in an online environment (Grijalva, Nowell, & Kerkvliet 2006). Researchers have also speculated that cheating will be more prevalent among online students because of faculty difficulty in directly overseeing students in the virtual classroom (Kennedy et al. 2000). However, Grijalva, Nowell, and Kerkvliet (2006) found that although students believed their peers were more likely to be cheating in an online course, the actual levels of cheating remained consistent with a face-to-face course.
The Landscape Architectural Accreditation Board (LAAB) is concerned about cheating, especially among schools in the United States, whose programs it accredits. Online education could mean that cultural considerations need to be considered when trying to create a culture of strong academic integrity. Studies have shown that cheating is not consistent across cultures. Chapman and Lupton (2004) found that U.S. university students cheated at a significantly higher rate than those from Hong Kong. Similarly, Russian students were found to be more than twice as likely to cheat as U.S. students (Lupton & Chaqman 2002). Even among countries and cultures with shared experiences and regional similarities, there can be markedly different attitudes toward cheating (Chudzicka-Czupała et al. 2013). The rise in international students in graduate LA and allied design educational programs could create additional variables to consider in regard to academic honesty in online LA education.
RESEARCH OBJECTIVES
As noted, the broad scope of learning typologies (e.g., studio, seminar, traditional lecture) in the
LA pedagogical delivery method makes it imperative to distinguish learning differences between design-based and non–design-based students’ pedagogical needs. Simultaneously, access, interactivity, online preferences, and academic integrity have been highlighted by reputable members of LA practitioners and scholars as primary concerns with online learning. It is important that online learning options for programs related to creativity retain the ability to enhance students’ knowledge acquisition (Park & Ko 2012). In light of these circumstances, this research seeks to determine:
the current assets and capacities for delivering online learning courses in LA (using access, interactivity, online preference, and academic honesty as analytical factors), and
the degree to which LA students’ online instruction needs differ from students in non-LA majors.
To achieve these goals, three years of survey-based research was analyzed using a high-enrollment online history of landscape architecture course. Both pre- and postcourse evaluations were used.
METHODS
Course Offering
The course used in examining these objectives is LAND240—History of Landscape Architecture I, taught biannually at Texas A&M University. Enrollment averages 180 students per semester. It is a general introduction to the history of human settlement and landscape design/planning, from prehistory to the nineteenth century, in locations primarily outside of North America. Global examples of renowned designed landscapes are introduced in the class and discussed in reference to their historical development and in regard to specific cultural and philosophical contexts. The current methods used for course material delivery and assessment involve mixed media and interactive online learning modules. Formative assessment questions are integrated in to each module, which are graded so that students watch and are examined on the contents of each lecture. To limit issues inherent in offering a new course, we selected a course that has been offered online for several years. Course refinements resulted from student feedback and critiques of course learning objectives, syllabus structure and content, and course schedule by the university’s Center for Teaching Excellence. The university’s core curriculum council biennially evaluates the course. It is in good standing with the university and consistently receives good evaluations.
All interactive modules used in the course were developed as SCORM (shared content object repository model) learning objects. SCORM “shared content objects,” or teaching materials that can be shared across different learning management systems, record the history of a student’s interactions with the course and can automatically grade objective questions (such as multiple choice, drag and drop, or true-false) and report resulting grades to learning management systems (Newman et al. 2016).
Design of the course’s multimedia dimensions used Articulate Storyline to develop the modules, which contained downloadable key terms, audio, images, video, assessment questions, and closed captioning. All modules were uploaded to a Blackboard classroom learning management system. Grades were stored in the Blackboard Grade Center through the eCampus platform. eCampus is a platform for storing and organizing course materials, and houses Blackboard and other software in one area that enables student interaction with course materials. Other analytic data, including student responses for formative module questions and student access information, were also recorded in eCampus.
Lecture modules, PDFs of the lecture slides, online cinematic screenings, YouTube videos, transcripts of lectures, and chapter readings per lecture module were all assembled as accessible information through eCampus. The lecture modules included closed captioning and interactive possibilities for engaging and advancing information by clicking interactivity buttons. These features required listening to completed audio descriptions of each slide prior to advancing to the next slide. Student performance in the class was analyzed through a combination of tests, quizzes, discussion forum/question postings, and lecture module scores.
Participating Student Samples
All students enrolled in the History of Landscape Architecture course between spring 2016 and summer 2018 were invited to participate in the survey. Data from course offerings spanning eight semesters over the three years were included to minimize the potentially confounding effects of seasonal variation and differences between regular and summer semesters.
A survey (hereafter referred to as the “presurvey”) was administered during the first week of class, prior to participants’ introduction to the online course platform. In the postsurvey, the same instrument was administered in the last week of class to gather student perceptions and experiences at the end of the course offering. Participation was voluntary, and a modest incentive involving extra credit was used to encourage participation. Regardless of their responses, students completing both surveys were given the same number of extra credit points. As a whole, presurvey responses were compared with postsurvey responses, but no attempt was made to link a single person’s responses from both surveys.
All students participating in and completing the entirety of the study were awarded the same amount of extra credit. To control for a social desirability bias, the tendency of survey respondents to answer questions in a manner they believe would be viewed favorably by others (that is, instructors distributing extra credit for participation), all survey data obtained were held strictly confidentially, and no personally identifiable information was reported. Student names were coded as personal identification numbers, which were used only for assigning extra credit points. Students were notified at the start of each survey about the confidentiality methods.
The surveys were administered using Qualtrics survey software. The response rates of the presurvey and the postsurvey were 74.4 percent and 76.3 percent, respectively. Survey responses with complete pre- and/or postsurveys were included in the final analysis, yielding a total sample size of 647.
As displayed in Table 1, the sample consisted mostly of students between the ages of fifteen and twenty-four, with a balanced distribution of men and women. Approximately 9 percent of participants were LA majors, and another 20 percent majored in design-related fields (e.g., architecture, urban planning, or environmental design). The majority of students (97 percent) had heard of online learning and used eCampus (95%) previously; however, approximately 70 percent had ever enrolled in a fully online course with no face-to-face class meetings.
Descriptive Statistics Among 647 Survey Respondents for Student Background and Prior Experience with Online Learning Approaches to Education
Survey Design and Analytical Measures
While the survey was distributed prior to the 2018 CELA Annual meeting panel, the four issues from this discussion (access, interactivity, online preference, and academic honesty or integrity) were used as an analytical framework to guide design of the survey data analysis. The comprehensiveness of the survey’s design in terms of questions pertaining to online instruction evaluation and academic integrity allowed survey data analysis to address the CELA panel themes with a careful and structured analysis of the data relative to the analytical measures. The presurvey included questions regarding demographics and preference for online learning. The postsurvey was composed of questions concerning demographics, preference for online learning, interactivity (including collaboration and engagement), and academic honesty (integrity), as well as additional questions not pertinent to this study.
Access was derived from student demographic data entered in both surveys, students’ quantitative and qualitative responses, and the Internet Protocol (IP) addresses registered with the survey entries. The preference construct was assessed in both surveys using seven questions to which respondents indicated the magnitude of their agreement/disagreement on a seven-point Likert scale. The interactivity construct (including collaboration and engagement) was examined using eight questions measured on a seven-point Likert scale. Academic integrity was assessed with three questions measured on a seven-point Likert scale. The key measures, the survey questions corresponding to the measures, and their locations in the survey instrument are listed in Table 2.
Analytical Measures Related to Online Learning Survey, Questions Examining These Measures, and Their Location Within the Survey Instrument
Data Analysis
Mixed-methods data analysis facilitated integration of quantitative and qualitative analyses and framed our interpretation of the findings.
Quantitative analysis
The quantitative analysis focused on understanding the extent to which students’ perceptions of preference, as measured on seven items, changed as a result of taking the class and what factors accounted for the observed patterns. A similar framework was used for analyzing data about interactivity (eight question relating to collaboration and engagement) and academic integrity (three questions). Descriptive statistics described the counts and percentages for categorical variables.
Pre- and postsurvey preferences for online classes were analyzed in a paired samples Wilcoxon signed-rank test. While a pre-post analysis generally involves use of a t-test to examine whether the means differ, it assumes normal data distributions. A Shapiro-Wilk test (p < 0.05) revealed that distributions of the response data exhibited a significant departure from normality. Therefore, a paired-sample Wilcoxon tests, which is a nonparametric statistical test to compare two related samples without requiring normality, were performed to examine the difference between pre- and postclass scores for preference, interactivity, and academic integrity.
A repeated-measures analysis of variance (ANOVA) was used to investigate whether changes in preference after taking the class differed by major and prior experience with online education. Multiple regression analysis was used to predict interactivity (including collaboration and engagement) outcomes based on student profile, experience, and preference. All statistical analyses were performed using R packages (R Core Team 2018).
Qualitative data analysis
Answers to the open-ended questions were pooled and analyzed using directed content analysis assisted by the MAXQDA 12 software package (Kondracki, Wellman, & Amundson 2002). Primary analysis categories (or themes) were selected based on the four analytical issues examined here (access, interactivity, online preferences, and academic integrity). A two-round coding process was performed, followed by charting and interpretation (Srivastava & Thomson 2009). The first round consisted of developing a preliminary thematic framework based on the four issues. Key concepts embodied in the text (referred to as “codes”) and themes emerging from the coded responses were captured in this phase. The second round of line-by-line coding (searching for key concepts) identified sections of the data corresponding to each code and theme. During this phase, the researchers continued to refine the thematic framework, allowing new codes to emerge. After completing the coding, the researchers summarized the frequencies of codes based on the thematic framework. The relationships between themes and codes were interpreted, and representative quotes were analyzed.
FINDINGS
Figure 1 provides an overview of the results of the qualitative analysis of open-ended questions. The varying sizes of squares denote the importance of this code relative to the others, which was calculated based on the percentage of participants mentioning this theme. For example, a larger square for “Selfpaced” (under “Interactivity, Collaboration, and Engagement”) in the non-LA column suggests that it was mentioned more frequently than the other codes for non-LA students. A comparison of the columns demonstrates that although the general patterns are consistent across the student groups, LA and non-LA students diverged in their focus on the main issues, which is explained further shortly.
Totals and comparisons of landscape architecture and non-landscape architecture majors from survey data.
Pre- and Postcourse Preferences for Online Learning
Enhanced preference for online learning
Figure 2 presents boxplots comparing the values of subject responses for the seven online learning preference statements in Table 2 between the pre- and postsurvey subject groups. Paired-samples Wilcoxon tests confirmed the trends evident in Figure 2. Students completing the survey after taking the class expressed significantly (p < 0.01) enhanced postclass preference for online learning for most of the variables: feeling great about the idea (p < 0.001), feeling less nervous about online learning (p < 0.001), feeling more familiar with the idea (p < 0.01), enjoying it more (p < 0.001), finding it more stimulating (p < 0.001), and preferring it to traditional learning (p < 0.001). The only variable that did not increase significantly for the postsurvey students was whether they looked forward to future online classes.
Boxplots comparing pre- and postsurvey group values for seven online learning preference measures in Table 2.
In their narrative comments, although most respondents did not comment explicitly on how their preference for online learning changed, many mentioned that the class was a positive experience. A few students said that they “liked the course more than I thought I was going to.” One mentioned how his attitude about online learning changed after taking the class: “It was an incredible experience and turned my fears into strengths. I will look forward to taking more such courses.”
Factors explaining pre- and post-survey online preference change
We also examined whether these changes in preference varied according to major (area of academic emphasis) and prior experience with online learning. Figure 3 shows the results of two key aspects of preference: feeling nervous about online learning, and preferring online learning over traditional methods. Upon completing the course, fewer LA students felt nervous about online learning, and more of them preferred online learning over traditional methods. Although non-LA students were less nervous and reported higher preferences for online learning at the beginning compared with LA students, they demonstrated similar positive attitude shifts post to taking this course.
Comparisons and contrasts in online learning anxiety levels and preferences for more online courses by LA and non-LA students responding to evaluation surveys given prior to and after completing an online course in the history of landscape architecture.
Examining whether change in preference for online education varied significantly across the majors of students enrolled in the course involved conducting a repeated measures ANOVA with student major as the between-subject factor and time (completion of pretest versus posttest survey) as the within-subject factor (Figure 4, left). The results confirm the significant primary effect of time on preference (F(1, 639) = 27.24, p < 0.001), but showed nonsignificant main effect of major (F(1, 639) = 1.75, p > 0.05), or timemajor interaction (F(1, 639) = 0.07, p > 0.05). Therefore, the enhancement of preference for online education after taking the class did not vary between LA majors and nonmajors.
Changes in preference score reported by LA and non-LA students as well as students who have or have not previously enrolled in an online course.
Similarly, we performed repeated measures ANOVA with prior online class experience as the between-subject factor and time as the within-subject factor (Figure 4, right). The results again show a significant main effect of time on preference (F(1, 639) = 95.09, p < 0.001), a main effect of prior experience (F(1, 639) = 74.53, p < 0.001), and a timeprior experience interaction (F(1, 639) = 5.10, p < 0.05). These results confirm that students having prior experience with online classes showed consistently higher preference, but those who did not have prior experience reported greater enhancement in preference after the class.
Access for a Broader Audience
Compared with on-campus courses, online education affords quality learning experiences to a broader audience with diverse backgrounds and needs (Allen & Seamen 2016). In analyzing survey responses, four main categories were identified by which online learning appealed to a broad audience: alleviating temporal, geographic, and disciplinary barriers and multimodal learning.
Alleviating temporal barriers
Approximately 40 percent of all survey responses mentioned being able to engage the course material on their own time schedule as a benefit of online learning. Some stated a need to work around employment and other course schedules. Other students mentioned being most effective at certain times of the day or week and thus enjoying taking modules and tests when they functioned best. This finding mirrors that found in previous studies about the value of online education to provide time flexibility to students (Matthews & Weigand 2001; Sagun, Demirkan, & Goktepe 2001).
Removal of geographic barriers
Students enrolled in the eight offerings of the course used in this analysis completed the course in seven different U.S. states, as well as countries in North America, Asia, and Europe (Figure 5). A total of 320 (49.5 percent) of the students completed the presurvey off campus. This ability to involve students from various geographic locations has consistently been one of the benefits of online education (Dave & Danahy 2000; Kvan 2001). In answers to open-ended questions about the course, 21 percent of participants identified the ability to learn off campus as a strength of online learning. They appreciated having the freedom to hold a job or internship far away, visit family, and travel while participating in the class. This flexibility is increasingly important in an educational system with increasing numbers of nontraditional students who work and have family responsibilities (Choy 2002). Even students who attended the class locally enjoyed taking it from the comfort of their preferred location and not spending time and resources commuting to campus.
Global locations of course attendees.
Reducing disciplinary barriers
In addition to geographic diversity, the course’s core curriculum status (along with its flexibility due to the online delivery method) also attracted students from various academic backgrounds. Attendees of the course represented sixty-four different majors. Only 9 percent (n = 55) were LA majors. Approximately 22 percent (n = 139) majored in design-related fields other than LA (for example, architecture, construction science, urban planning, and environmental design). Students perceived the class not only as an introduction to international landscape architecture but as an avenue for exposure to art, cultural, and humanities. For example, a student from electronic engineering commented that the class “was an enjoyable course to come to after all my engineering classes, I always looked forward to doing my work for this course, unlike many others.” Another student majoring in psychology mentioned that the material was “something I have never known about and was very interesting to learn about.” Many students mentioned that they learned about LA and became interested in the profession after taking this class.
Multimodal learning
Unlike traditional classrooms, where students have to explicitly follow the planned activities of the class, online learning enables them to customize their experience based on their learning style. Students self-identified as different types of learners: visual, auditory, reading/writing, and hands-on. Because the class provided slides, audio-narrated slides, transcripts of the audio, images of landscape designs, YouTube videos, and interactive quizzes, students could choose any combination of information conveyance in customizing their approach to understanding and retaining knowledge effectively. Some of the different approaches mentioned were that they listened to or muted the audio; read, printed out, or wrote notes on the transcripts; and scanned through and carefully examined the images. Some students liked “seeing visuals along with hearing the lecture,” whereas other students found it helpful that “the modules included a transcript to follow along.”
Because students’ evaluations of the accessibility of the course were predominately positive, no primary theme emerged regarding how online learning may limit access. A few negative comments were related to the fact that the tuition of this particular online class was higher than that of traditional classes. One student mentioned this class was “Way too expensive compared to other courses, I almost did not take it because the cost difference was so great.” This, and similar statements could vary by university because not all universities assign an additional fee to online courses (as does the University of Georgia).
Interactivity, Collaboration, and Engagement
Perceived advantages in interactivity
In their answers to open-ended questions, students identified nine reasons that online learning is more interactive and engaging than face-to-face learning (Figure 6). Individual control over the material delivery contributes to the interactivity of online learning. Control factors include self-paced learning; the ability to pause, stop, and revisit lectures and videos; and the opportunity to advance through course material ahead of the prescribed schedule. These features allowed students to actively pursue their own schedule instead of passively following during class periods. Although students may be active learners in a traditional classroom setting, online learning does not restrict them to a fixed educational schedule.
Percent of LA and non-LA respondents reporting perceived advantages of various strategies for enhancing interactivity and engagement.
Students mentioned they can “review the materials again and again to understanding the content” or “look up words in a dictionary.” They commented on the abilities to “learn as much as you can in a day instead of working on the schedule of the instructor” and self-guide their learning venture to “dig deeper into some aspects.” Students also appreciated the interactivity of the format and content. They enjoyed multi-media materials and found them informative, and they liked the clear and consistent organization and multiple knowledge checks along the way. For example, students commented that, “The modules were interactive and the syllabus was structured very well,” and they benefited from the “simplicity and consistency of it.” As quizzes were built into the lecture modules, students mentioned that they could “gauge how well I was doing based on lecture quizzes, and then quizzes, and then ultimately the exams.” Finally, students also perceived online learning to be more interesting and less stressful. Students found their self-directed interactions with the materials interesting and stimulating, and they felt less stress during the learning process. In the context of student majors, Figure 6 illustrates that LA students and non-LA majors perceived similar benefits, except that non-LA majors reported the ability to self-pace their study as being much more important than LA students.
Perceived barriers to interactivity
Although students valued the strengths of online learning, they also identified barriers to interactivity and engagement while learning online (Figure 7). These included many requirements for accountability and time management; lack of face-to-face instruction, peer connection, and immediate access to help; technology glitches; getting distracted; and feeling as though they had a less personal learning experience. Many of these findings mirror the findings of George (2018), whose study of an online studio course found that the most consistent student concerns were related to social and communication issues. The confirmation of these findings here illustrates the need to improve social interactions in online learning environments.
Percent of LA and non-LA respondents reporting perceived barriers to interactivity (including collaboration and engagement).
Although non-LA students reported enjoying the self-pacing aspect of the class, they also found that it required more accountability and advanced task management skills. Conversely, LA students identified the lack of immediate access to help as the greatest barrier. This is not surprising, as LA students are used to the more immediate feedback they receive from faculty and peers in the studio.
Suggestions for overcoming barriers to interactivity
Students identified strategies to increase interactivity, collaboration, and engagement (Figure 8). These included reminders to improve scheduling, more group projects, use of a discussion board to enable connection with each other, improved technology, and more interactive lecture modules. Consistent with the findings on barriers, non-LA students encountered more accountability issues and voiced a greater need for assignment reminders. Many non-LA students mentioned that constant and consistent reminders of tasks and due dates are extremely helpful for their success. LA students, on the other hand, suggested increased use of the online discussion board, where they could more readily interact with fellow classmates or the instructor. This suggests that LA students may expect a higher level of communication within the course. Rather than implementing a discussion board, online design courses might consider using social media platforms. These platforms produce deeper discussion of topics, which is particularly valuable in dealing with the complexity of issues associated with landscape architecture projects (George 2017).
Percent of LA and non-LA respondents reporting various suggestions for enhancing interactivity, collaboration, and engagement.
One student wished that “there were individual discussions from students about the modules so that students could interact with one another more, even though it is an online class.” Others wanted “a live chat with the professor” or “video chat with the class” at certain times. Several encountered issues with incompatible web browsers and embedded multimedia material and suggested fixing technology issues and ensuring the use of stable platforms to host the course. Making the lecture modules even more interactive was also among the popular improvements suggested.
Factors influencing interactivity, collaboration and engagement outcomes
To gauge the perceived interactivity, collaboration, and engagement outcomes, we analyzed the quantitative data from the questionnaire (Table 3). The majority of participants agreed that online learning supports self-paced learning and is more motivating. This was typically tied to the fact that the course was self-motivated and assignment completion and access to lecture materials was completely up to the students. Regarding collaboration, most students perceived their interactions with peers to be equal to or better than those in traditional classrooms, although a fair number perceived interaction with the instructor as more limited. Overall, students reported efficient and improved engagement outcomes.
Agreement Among Survey Respondents with Specified Characteristics About Interactivity, Collaboration, and Engagement Outcomes
To examine which factors influenced interactivity, collaboration, and engagement, we created a single composite interactivity measure by averaging these eight items. A regression analysis was conducted to predict the composite interactivity score based on student profile, preference, and style of learning variables (Table 4). As noted, the perceived increased ability to cheat in online courses is commonly noted as a reason for caution when using online teaching. For this reason, being more tempted to cheat than in a traditional lecture based course and the perception of others cheating more was also included in the analysis.
Predicting Interactivity, Collaboration, and Engagement from Profile, Preference, and Learning Experience
The results show that women students tended to display greater perceived outcomes than men, whereas lower division students perceived online learning as more interactive and engaging than did upper division students. Students who were more active during the learning process also demonstrated higher perceived interactivity and engagement.
For example, those who took notes displayed an eleven-point higher perceived effectiveness score than peers who did not. Students who reported communicating with their classmates and making new connections also perceived the class to be more effective. Last, feeling tempted to cheat or believing others cheated proved to be a negative factor for engagement outcome.
Academic Integrity
Concerns about cheating
Based on answers to the open-ended questions, cheating did not arise as a main concern of students. The majority (85.0 percent) did not find it tempting to cheat or they held neutral views on cheating (Figure 9, left). These rates are considerably lower than those found in previous studies (King, Guyette, & Piotrowski 2009; Rozycki 2006). Although the percentage of students who believed others cheated was slightly higher than that of students who were tempted to cheat, the majority (80.7 percent) disagreed with or held neutral views on the cheating behavior of other students (Figure 9, right). These findings align with Grijalva, Nowell, and Kerkvliet’s (2006) findings that students believe others are cheating at a higher rate.
Percent of students agreeing or disagreeing with two perceptions of cheating.
Suggestions on preventing cheating
Although preventing cheating can be a challenge in online learning, improvements to the assessment process could resolve or mitigate this issue. Students provided six different possible suggestions, including use of a larger question pool and randomizing orders of questions and answer choices, web proctors or on-campus tests, browser lock-down timers, more open-ended and thesis-type questions, restating the student honor code, and imposing browser window restrictions (Figure 10). A larger question pool was recommended by more LA students, and using web proctors was suggested by a larger percentage of non-LA major students, which is a strategy several researchers have identified to mitigate cheating (Olt 2002). Another student suggestion, reminding students of academic integrity, has been found by researchers to be effective at reducing cheating (King, Guyette, & Piotrowski 2009). However, the use of web proctors seemed controversial, as a few comments mentioned the services being unstable: “Please don’t try and avoid cheating by using something [an online proctor service], that service is horrible and very time-consuming!” Olt (2002) suggests that one of the best ways to reduce cheating is to create evaluation mechanisms that focus on process, as opposed to rote memorization or final products. Such an approach would seem well suited to the field of landscape architecture. Whichever type of evaluation procedure is used, multiple solutions can be combined to build a system that prevents cheating.
Percent of LA and non-LA majors suggesting various strategies for preventing cheating.
CONCLUSIONS
This article explored the capacity of online learning courses to deliver content that is relevant to the education of LA students. It also discussed and distinguished the capacity of online delivery to meet the needs of LA versus non-LA students.
Student preference for online learning is increasing. Many included in this study (especially those who had never taken a prior online course) reported anxiety before the course’s beginning but were more confident at the conclusion of the course. Students with prior experience in online classes showed consistently higher preferences for online learning, and those who did not have prior experience reported greater enhancement in preference after the class. This preference seems to be consistent across all majors, including design-related ones.
The findings suggest a series of salient points and concerns. First, online instruction has the potential to alleviate temporal barriers by increasing the flexibility of class times that is not currently afforded by traditionally scheduled meeting times. This confirms the findings found by previous studies (Kvan 2001; Li & Murphy 2004) and is potentially important for increasing enrollment.
Second, online learning virtually removes geographic barriers, thereby increasing the student ability to pursue LA courses irrespective of location. This facilitates recruitment of students into the LA major, an important step in enhancing the number of students pursing the profession. Although the removal of geographic boundaries provides an opportunity to increase the diversity of viewpoints held among students in a class, use of a standardized online delivery mode works at cross-purposes to this benefit. Instructors need to design their online courses to leverage the culturally diverse perspectives of their students.
Third, in the online learning course, all students receive identical audio and video materials through prerecorded modules. This is noteworthy in ensuring consistent instruction and for off-loading significant time and resource demands from the instructor in the form of content preparation and delivery. This is an often overlooked benefit of online education in which faculty have increased capacity to communicate with students and critique their work compared with a traditional classroom setting (George, Shelton, & Walker 2017).
Fourth, student-paced scheduling seems to increase self-motivation, as students can better control their engagement with lecture material, interaction with course content, and completion of assignments and projects. This flexibility is also important for increasing enrollments because it makes it possible for students to enroll in courses they may not otherwise be able to pursue (George 2018). This freedom, however, can come with an increased rate of late or incomplete assignments, compared with traditional courses. It is important that faculty establish clear expectations relative to submission deadlines. Developing a system of reminders for each assignment due date seems to limit this condition. However, similar to findings of other studies (George 2018), we found that the self-paced atmosphere and easy repeatability of information in the online format provided a forum for repetition of information.
Fifth, although the interactive multimedia delivery of the course examined here proved effective in enabling students to customize their experience based on their learning style, course instructors often need an advanced level of software skills to complete such an undertaking. Use of multimedia options in presenting the diverse information required in contemporary LA education also requires a high degree of technical and organizational capabilities from instructors. These technological capabilities also need to be fine-tuned to ensure academic honesty.
Although cheating did not arise as a major concern among all students in this course, many expressed a belief that some students were cheating. Cheating in an online course creates a lack of mutual trust among students that could contribute to an unhealthy learning environment. Such an environment may encourage students to cheat if they believe it will advantage them relative to their peers (Grijalva, Nowell, & Kerkvliet 2006). This suggests that the perception is still present and thus needs more attention.
Finally, methods of reinforcing and increasing online student–teacher interaction need to be further developed and employed. While most students perceived that their interactions with peers were equal or better than those in a traditional classroom, some perceived that interaction with the instructor was limited. These findings contrast with those of George (2018), who studied an online studio course where interstudent communication was found to be ineffective while student–faculty communication was effective. This difference may be explained by the relative role of communication in a lecture-based versus a studio-based course. In the history course examined here, communication between students was often received as less critical to academic achievement and growth. In a studio course, communication among students is a critical and integral component to developing the culture of studio learning (Schön 1983).
There are a few noteworthy findings specific to LA students. Both LA and non-LA students reported a strong preference for the self-pacing capabilities available with online instruction. However, more LA majors than nonmajors accepted the increased accountability and advanced task management skills necessary for course completion. This is an important finding because successful professional landscape architects must be organized, self-motivated, and able to meet self-imposed deadlines. Well-organized and deadline-driven online instruction nurtures and develops these attributes. The increased flexibility of online education allows participation of LA students from other universities. LA students registered at the home university who are temporarily off-campus pursuing required or elective professional internships may continue to complete courses in their major.
Although enhancement of preference after taking the class varied little among the majors of enrolled students, LA majors found the lack of access to professors a stronger barrier than did non-LA majors. Mechanisms to increase student–professor and student–student interactions for LA students merits further investigation. Future studies might compare the effectiveness of different methods for increasing online interaction. As presented in other studies (George 2017), further exploration and application of social media applications and other popular communication software may facilitate lively interaction in both lecture and studio design courses.
Finally, as noted, many students felt less nervous about online learning after taking the online course. Successful recruitment of new LA majors from other fields through online education may be affected by the differences in the level of anxiety about the use of online formats between LA and other majors.
The specific nature of studio–based education is the backbone of most LA curricula and may limit extension of findings of research focused on adapting a traditional lecture-based course into an online learning platform. Extension of inferences from this study should be cautiously used in developing online learning platforms for studio-based courses. Although the sample was broad in regard to the number of majors, the survey was conducted at one university, in one course, and did not directly address design studio-based learning. Future studies can apply this methodology to a broader range of LA courses and institutions.
As noted, all survey data were confidential, and no personally identifiable information was reported. Student names were coded as personal identification numbers, which were only used for providing extra credit scores. Students were notified at the start of the survey about the confidentiality methods used. While this approach is accepted best management practice, an inability to guarantee complete anonymity can sometimes spark social desirability bias, in which survey respondents answer questions in a manner they believe will be viewed favorably by others. Knowing that their responses were linking to their university identification numbers could have increased this bias.
Another limitation is related to the fact that all students included in this study were taking an online course in landscape architectural history. There was no possibility of comparing the learning styles and successes of students completing an online course with those pursuing a traditional format for delivery of educational content. Because there was no control group, the changes in the pre-post responses could be attributable to the cognitive development and greater exposure to digital tools. Future studies may include a control group using traditional instructional methods to reduce these threats to internal validity.
AUTHOR CONTRIBUTION
Galen Newman led survey distribution, data gathering, data interpretation, assisted with literature review and managed overall organization, editing, and manuscript assembly; Benjamin George led the literature review portions and assisted with result interpretation; Dongying Li led the statistical modeling and data analysis and assisted with result interpretation. Zhihan Tao, Siyu Yu, and Ryun Jung Lee assisted with data collection, data cleaning, data analysis, and basic result interpretation.
PEER REVIEW STATEMENT
This submission was peer-reviewed by four peer reviewers selected by the Editorial Office. Their contributions are gratefully acknowledged and appreciated.
ACKNOWLEDGMENTS
None
Footnotes
Galen Newman is an associate professor at Texas A&M University, where he also serves as program coordinator for the bachelor’s of science in Urban and Regional Planning Program, associate department head for the Department of Landscape Architecture and Urban Planning, and director for the Center for Housing and Urban Development. His research interests include urban regeneration, land use science, spatial analysis, and urban design. He teaches courses related to history of landscape architecture, advanced digital representation, and design studio.
Benjamin George is an assistant professor in the department of Landscape Architectureand Environmental Planning at Utah State University. His research focuses on the use of virtual reality in landscape visualization, online design education, and historical landscapes. He is the creator of the Digital Library of Landscape Architecture History, an online repository of virtual tours of important historic landscapes from around the world.
Dongying Li is an assistant professor in the Department of Landscape Architecture and Urban Planning at Texas A&M University. Her research examines the relationships between the characteristics of the built environment and mental health and well-being. Specific topics include nature and mental health, nature and child development, and equity of access to environmental amenities.
Zhihan Tao is a doctoral student in the Urban and Regional Sciences program at Texas A&M University. He serves as graduate teaching assistant for history of landscape architecture courses. His research interests include landscape performance, design pedagogy, and low-impact development. He teaches history and studio-based courses.
Siyu Yu is a doctoral student in the Urban and Regional Sciences program at Texas A&M University. She serves as a graduate research assistant and examines plan evaluation, ecological vulnerability, statistical analyses, and GIS-based analytical methods.
Ryun Jung Lee is a lecturer in the Department of Landscape Architecture and Urban Planning at Texas A&M University. She has a doctorate in urban and regional sciences. Her research interests include land use planning, spatial analytics, and urban design, with a focus on urban vacant land and marginalized communities. She teaches courses in landscape history, planning theory, and design studios.
