A Systematic Literature Review of Trends in Research and Practice Among Asynchronous Online Course Offerings in Formal Engineering Curricula

Publications per year

From 2011 through 2020, there was a general increase in the number of articles published on online engineering education research per year, reaching a high in 2020 (Figure 2). This trend is encouraging because it suggests increased interest in the online learning format by engineering scholars and practitioners over time in their respective research and teaching activities.

Figure 2

Publication type and publication outlet

Among the 39 articles reviewed for this study, 69% were published as conference proceedings and 31% as journal articles. Most conference papers sampled in this systematic review were published in conferences sponsored by the American Society for Engineering Education (ASEE) (48%) and the Institute of Electrical and Electronics Engineers (IEEE) (22%), with the remainder published in other venues. Further, the journal articles sampled in this study appeared in the following journal outlets: Computer Applications in Engineering Education (25%), Education and Information Technologies (8.3%), Advances in Engineering Education (8.3%), Chemical Engineering Education (8.3%), IEEE Transactions on Education (8.3%), IEEE Transactions on Learning Technologies (8.3%), Internet and Higher Education (8.3%), Journal of Online Engineering Education (8.3%), Sustainability (8.3%), and Online Learning (8.3%).

Country affiliation of first author

Table 1 shows that the articles selected for this review featured first-authors from fourteen countries. The majority of first authors heralded from the US (53.9%), followed by Spain (12.8%) Australia (5.1%), and 2.6% for each of 11 remaining countries. We acknowledge that the high occurrence of online engineering education research originating from the US in our search could be influenced by the fact that we included articles written only in English. In addition, practitioners in other countries may engage and invest in online engineering education but have different pressures, incentives, or research support infrastructure to publish on this work.

Engineering programs and courses

Table 2 lists the engineering programs and courses studied in the 39 articles. Across all fields, Mechanical Engineering (13.3%) was the most often studied program, followed by Computer and Telecommunications Engineering (13.3%), Engineering Management (10.0%), First-Year Engineering (6.7%), Systems Engineering (6.7%), and 3.3% for each of nine remaining programs. In Table 2, the word “course” refers to a single class offering in the specified subject. Further, the courses studied span undergraduate and graduate courses, as well as theory-based and laboratory-based courses. These findings indicate the applicability of online learning to diverse courses and programs within engineering. (Note: nine articles in the sample were not included in Table 2 because they comprised literature reviews and general online engineering-based studies wherein specific programs or courses were not indicated).

Theoretical frameworks

Fifteen articles used a theoretical framework, nine used a conceptual framework grounded in literature, and the remaining fifteen did not use a framework in their study. For papers that employed a theoretical framework, the frameworks they used are summarized in Table 3. Notably, frameworks were not repeated across these studies, suggesting a wide range of theoretical perspectives being applied in the online engineering learning space and indicating an opportunity for further testing and application. However, while none of the frameworks repeated, the importance of peers, faculty, and learning environment for online student engagement was mentioned across multiple articles. Five of the fifteen articles that did not use a framework were literature reviews, for which a framework is typically not used. The remaining articles that did not use a framework discussed the design of a new course or new interventions within a course.

Research foci and research methods

Table 4 summarizes the different research foci and methods used in the sample articles. Five kinds of articles emerged from the set: literature reviews (12.8%), articles focused on the description of new or existing courses (33.3%), articles focused on the description of new or existing interventions (30.8%), articles focused on the description of new or existing programs (5.1%), and more fundamental research that transcends specific courses and programs (17.9%). Studies marked as “no research” in Table 4 were descriptive and did not include original data collection and analysis.

The five identified literature reviews in online engineering education cover topics including the teaching of online laboratory courses (Badjou & Dahmani 2013), the measurement of quality online education (Danaher 2014), holistic online instructional design (Kiridena et al. 2014), sustainability challenges in online engineering education (Perales Jarillo et al. 2019), and the prevention of academic cheating in online courses (Siddhpura & Siddhpura 2020).

The thirteen articles focusing on a new or existing course tended to use both qualitative and quantitative research methods. The qualitative studies under this category collected open-ended student responses or instructor reflections about their course perceptions and experiences. Likewise, the quantitative studies under this category surveyed students about their course perceptions, while studies mixing qualitative and quantitative methods collected student perception, student course performance, and student teacher evaluation data. Five of the eight studies utilizing quantitative or mixed methods reported descriptive statistics only; the remaining three included simple inferential statistical tests in their analyses.

The twelve studies focused on interventions skewed more heavily quantitatively in their research methods. Five of these studies present interventions related to feedback and assessment, whereas the other seven describe new tools and technologies embedded into the classroom or laboratory. Data collected in these studies include (1) open-ended and survey responses related to students’ perceptions about the course, the intervention, and gains in their conceptual understanding; (2) student course performance and completion data; and (3) student usage and interaction data (e.g., with the instructor). Of the ten studies employing quantitative or mixed methods, six used inferential statistical analyses to evaluate the effectiveness of their intervention on students.

Two of the 39 articles reviewed for this study focused on program design or improvement. One paper detailed the development of a master’s-level program in manufacturing systems engineering (Badurdeen et al. 2015). The other described the implementation of a course equivalence program that allows students in a master’s-level systems engineering program to fulfill their degree requirements with course credit from other institutions (Zhang 2020). Both papers were descriptive, containing no student or instructor data.

Lastly, seven articles focused on what we define as “fundamental research,” that is, research that transcends specific courses or programs to increase general knowledge related to online engineering education. All papers in this category included a quantitative component to their data collection and analysis and tended to employ more advanced statistical techniques. Further, papers under this category were more likely to use institutional, programmatic, or instructor-based data to support their analyses.

Study populations and participant demographics

Table 5 shows the study populations included among the 39 papers in this systematic review. Twelve (50.0%) studied only undergraduate student populations, five (20.8%) studied only graduate student populations, three (12.5%) studied both undergraduate and graduate student populations, one (4.2%) studied employees in the workforce, and three (12.5%) studied student populations without specifying their academic level (undergraduate or graduate). Further, two papers (5.1%) featured instructors as their population, while another two papers (5.1%) featured US institutions with online master’s engineering programs as their population. The remaining eleven papers in the review (28.9%) contained literature reviews and other descriptive works, thus not including participant data.

Of the 24 articles featuring student populations, only nine (37.5%) reported information about students’ demographic backgrounds. All nine reported students’ gender identities, four reported students’ racial and ethnic identities, and four reported students’ ages. The remaining fifteen (65.2%) did not report any participant demographic information. These findings highlight the need for greater reporting of demographic information when presenting online engineering education research involving students; such information would allow for better contextualization and understanding of specific student experiences related to online learning.

Sampling approaches and sample sizes

Table 6 presents the sampling methods and sample sizes for the studies referenced above, categorized by research foci (refer to Table 4). Sample size ranges in Table 6 are based on studies for which sample sizes were indicated. The results reveal a tendency for studies about courses and interventions to draw data from just one course and, as such, to rely on smaller sample sizes. Alternatively, data for fundamental research in online engineering education tends to come from multiple (more than 2) courses and/or institutions, from which larger datasets are available to conduct the associated analyses (e.g., regression modeling). These findings suggest both a potential to expand the generalizability of results from single courses and interventions by engaging instructors from other courses, programs, and institutions in replication and extension studies, as well as the need for more fundamental research in online engineering education to expand current knowledge to beyond what we can learn from isolated efforts.

Theme 1: Content design and delivery

Twenty-one articles addressed the elements of online engineering course design and delivery. Together, these articles highlight that teaching online requires a different approach than teaching in the face-to-face format and deserves more attention to fully leverage the online modality.

We categorized fourteen articles under this theme as describing the design and delivery of online fundamentals courses (e.g., Balagiu & Sandiuc 2020; Bir & Ahn 2017; Bozkurt & Helm 2013; Chen et al. 2018; Fatehiboroujeni et al. 2019; Kiridena et al. 2014; Minichiello et al. 2013; Purwar & Scott 2019; van de Vegte 2017). These studies provide practical insights into the development of new supportive technology for blind and deaf engineering students (Batanero et al. 2019), the incorporation of learning about engineering disasters in a multidisciplinary online course (Halada 2017), the analysis of a support distance learning program in mathematics (Matzakos & Kalogiannakis 2018), the implementation of simulation-based programming to promote self and co-regulated learning (Pedrosa et al. 2020), and the use of instructional videos in an online engineering economics course (Pohl & Walters 2015).

We categorized an additional five articles under this theme as describing the design and delivery of online laboratory courses (Andersson & Logofatu 2018; Astatke et al. 2011; Badjou & Dahmani 2013; de la Torre et al. 2020; Uribe et al. 2016; Zhang 2020). These studies cover the use of computational simulations (Badjou & Dahmani 2013; Uribe et al. 2016), problem-based learning (Andersson & Logofatu 2018), remote or virtual laboratories (Astatke et al. 2011; Badjou & Dahmani 2013; de la Torre et al. 2020), home kits (Badjou & Dahmani 2013), and residential lab experiences (Badjou & Dahmani 2013) as methods for providing online students access to laboratory experiments. Lastly, two articles (Badurdeen et al. 2015; Zhang 2020) discussed the design and implementation of new systems engineering degree programs.

Exemplar studies. Exemplar studies under the content design and delivery theme include two articles highlighting the importance and use of videos in online engineering fundamentals courses. One such study is Purwar and Scott (2019), who presented the design, development, and implementation of an online sophomore-level engineering dynamics course. The course was offered over six weeks as eight modules, each containing eight to ten videos explaining course concepts and problem-solving approaches. In addition, each module included homework assignments and summative quizzes that contributed to students’ grades. Students were given opportunities to interact (ask questions and/or discuss) with peers and the instructor using a web-based forum called Piazza. In another exemplar study, Pohl and Walters (2015) explored the use of instructor-developed videos to teach economic decision-making to engineering graduate students. In the course, lecture videos provided an introduction, motivation, and theoretical background for course content, while tutorial videos included working example problems. The authors argued that posting lectures and tutorials as separate videos reduced the length of each video, made the purpose of each video more apparent, and helped students understand each video’s content better.

Exemplar studies under the content design and delivery theme also include two articles that highlight the use of technology and problem-based and collaborative learning pedagogies in online engineering laboratory courses. Astatke, Scott, and Ladeji-Osias (2011) discussed using Mobile Studio Technology to enable electrical and computer engineering students to conduct laboratory experiments online. Students also had to demonstrate their design and circuit for each experiment to the instructor using Adobe Connect software. Separately, Andersson and Logofatu (2018) applied problem-based learning to an Introduction to Natural Sciences laboratory course in mechanical engineering. In this course, students were divided into groups and asked to solve chemistry-related problems using a seven-step process. Students communicated with their group members through chat forums, web conferences, and email to solve each problem.

Research implications. A potential direction for future research based on this theme could be to determine how learners perceive instructional videos categorized as lectures and tutorials and what aspects help them best engage with the course content and enhance their learning. Additionally, research examining the relevance and applicability of the approaches (laboratories using simulations, remote laboratories, home kits, and residential lab experiences) to teach different engineering topics would aid in designing and developing approaches that support a variety of engineering courses. Finally, more research is necessary into the unique skill sets required for an instructor to develop and deliver online courses in a learner-centered format successfully.

Practice implications. The studies under this theme offer several practice implications for instructors designing online engineering fundamentals and laboratory courses. First, instructors are encouraged to incorporate the following six elements into their course design: (1) clear teaching roles and expectations, (2) use of a learning management system (LMS) platform, (3) integrated assessment and feedback, (4) integrated opportunities for student accountability, (5) integrated opportunities for student involvement and participation, and (6) a safe environment for discussion (Fatehiboroujeni et al. 2019; Halada 2017; Purwar & Scott 2019). Second, instructors can embed quizzes (or another form of assessment) in online videos to monitor if students view the videos, assess their conceptual knowledge, and enable students to reflect on their learning (Purwar & Scott 2019). Third, instructors can keep instructional videos short and focused, use effective visual slides, include audio and video of the instructor, and provide an introductory overview of the video content (Pohl & Walters 2015). Lastly, instructors can ensure that online laboratory courses are low cost to the student, do not compromise student learning, include reasonably achievable goals, provide adequate online demonstration, minimize risk, and provide guidance to students through assignments and feedback (Andersson & Logofatu 2018; Uribe et al. 2016).

Theme 2: Engagement and interactions in online courses

We categorized six articles under student engagement and interaction in online engineering courses. All articles underscored the importance of online student engagement and support the notion that students have better learning opportunities and experiences when they positively interact with their course content, student peers, and course instructor (Avanzato 2017; Fatehiboroujeni et al. 2019; Fu 2019; Odom et al. 2019; Schutz et al. 2018; Yousuf & Conlan 2017).

Exemplar studies. We chose the exemplar studies for this theme because they centered on student engagement, focusing on interactions with course content, other students, and the instructor. Fu (2019), an exemplar of this theme, proposed a Trifecta Framework of Engagement based on the three pillars of student-to-content, student-to-student, and student-to-instructor engagement. Fu adopted this framework in the operations management course of an online graduate-level engineering management degree program. Students in the course were tasked with reading materials, participating in online collaborative sessions, contributing to threaded question-and-answer discussion boards, watching instructional videos, and completing all quizzes, exams, assignments, and a group project. In the article, Fu demonstrated that these activities can significantly improve student-to-content, student-to-student, and student-to-instructor engagement. Students also reported that the course promoted curiosity, critical thinking, and problem-solving skills.

Another exemplar, Fatehiboroujeni et al. (2019) conceptualized student engagement as a function of time spent on different course activities, including interaction with course content, peers, and the instructor. They developed instruments to measure student motivation and engagement in two mechanical engineering courses and made two discoveries. First, students dedicated the most time to watching videos and assignments related to lectures and labs and the least to activities that were neither assessed nor graded, such as discussion with peers, optional problem sets, and reflection questions. Second, one-on-one student-to-student interactions (e.g., asking another student for help understanding course material, explaining course material to one or more students) were high, and student participation in the instructor-generated online discussion boards was minimal in both courses.

Research implications. Potential directions for future work include analyzing how student interactions influence student learning and engagement at different points during a course; determining the optimal nature and amount of student interaction with their course content, student peers, and course instructor to maximize student learning and engagement; and examining how the quality and type of student interactions in their online course enhance student learning and course completion. Further, despite the importance of student engagement in online courses, little work has provided specific measures, formulae, or frameworks for calculating online student engagement scores. One exception is Kittur et al. (2021), who computed online undergraduate engineering students’ engagement scores based on their patterns of interaction with their course learning management system (LMS).

Practice implications. Articles under this theme highlighted that instructors of online engineering courses should strive to intentionally create opportunities within online courses for students to interact with the course content, other students, and the instructor, as these interactions play a significant role in enhancing students’ engagement, learning, and success rate (Avanzato 2017; Fu 2019). The studies under this theme provide suggestions for increasing student engagement and interaction. For example, instructors can use different techniques to involve students in the course and encourage participation, such as interaction with the instructor, group activities, and poster sessions (Avanzato 2017). They can increase student participation in discussion forums by assigning credit to student responses to discussion board items and providing examples that initiate discussion and sharing thoughts and ideas (Fatehiboroujeni et al. 2019). Instructors can adopt tools such as visual narratives, which motivate student engagement by providing personalized information about course engagement to date, resources used, and time spent on activities (Yousuf & Conlan 2017). Finally, they can monitor student logins to their course learning management system (LMS) to better understand their engagement. Odom et al. (2019) revealed a medium correlation between the average number of times students are expected to log into their LMS and institutional student engagement score, suggesting that requiring students to visit their LMS more often will lead to increased engagement in the course and their education overall.

Theme 3: Assessment in online courses

We classified eight studies under assessment. Articles described the assignments, quizzes, midterms, final exams, online discussions, and projects used to formally assess student learning (Balagiu & Sandiuc 2020; Chatterjee et al. 2016; Cooper et al. 2020; Danaher 2014; Fu 2019; Purwar & Scott 2019). One article detailed the instruments used to assess students’ conceptual knowledge and identify potential causes for misconceptions (e.g., Pamplona et al. 2018), while yet another article provided an overview of best practices for assessing academic misconduct (Siddhpura & Siddhpura 2020). Articles under this theme also discussed the post-course evaluation of student learning through surveys, questionnaires, and other relevant instruments (Cooper et al. 2020; Fu, 2019; Purwar & Scott 2019).

Exemplar studies. We chose the exemplar studies for this theme to highlight the importance of assessments, the drawbacks of poorly designed assessments, and the design and implementation of formative assessments to support learning. Designing robust assessments is important since poorly designed assessments can lead to academic misconduct because they may make it easier for students to cheat. In their review article, Siddhpura and Siddhpura (2020) analyzed various forms of academic misconduct, student motivation for involvement in academic misconduct, and ways to identify academic misconduct in online engineering. The authors argued that plagiarism and contract cheating in online assessments could be minimized using three kinds of approaches: (1) a virtues-based approach, in which students are encouraged not to cheat; (2) a prevention approach, in which prudent course design and delivery and effective online assessments minimize the likelihood that students can cheat; and (3) a police approach, in which students involved in academic misconduct are penalized.

In another study, Chatterjee, Kamal, and Wang (2016) illustrated the design and implementation of formative assessments to support learning in an online graduate computer engineering course. The assessments included asynchronous online discussions, virtual labs, open-ended module assignments, and a culminating project. The instructor reflected on the course that these assessment activities together promoted student creativity and critical thinking.

Research implications. Differences between the face-to-face and online learning modalities bring challenges and opportunities to explore differences in the effectiveness of their assessments. Further research in this area is required to answer questions about the effectiveness of assessments designed for face-to-face courses when used in the online learning format and to understand what changes must be made (if any) in the design of assessments to facilitate the translation from the face-to-face learning to the online learning format. Separately, there is a need to investigate further the strategies for assessing and addressing student misconceptions to enhance student learning in the online space (Pamplona et al. 2018).

Practice implications. Since monitoring student behavior during assessment in online courses is comparatively more difficult than in face-to-face courses, and poorly designed assessments may make it easier for students to cheat and can lead students toward academic misconduct (Siddhpura & Siddhpura 2020), adequate attention must be given to the design of effective assessments. Creating awareness about academic integrity, establishing the instructor’s role as both advisor and mentor, encouraging lifelong learning, creating awareness about the benefits and drawbacks of information available online, and setting high academic integrity expectations are some strategies that instructors can use to reduce academic misconduct (Siddhpura & Siddhpura 2020). Further, instructors should include different assessments in their online courses to evaluate students’ understanding of course content, just as they might in face-to-face courses. Open-ended assignments, real-world laboratory experiences, and course projects can supplement traditional assessments, such as exams and quizzes, to boost students’ conceptual understanding (Chatterjee et al. 2016).

Theme 4: Feedback in online courses

The five articles under this theme address the importance of two types of feedback: the feedback the instructor provides students throughout the course and the feedback the students provide the instructor during and toward the end of the course. A few articles discuss the differential importance of instructors providing students with text-based, interactive, and automated feedback (Rutz & Ehrlich 2016; Sancho-Vinuesa et al. 2018). Other articles discuss the importance of collecting student feedback in online courses to evaluate the course’s effectiveness (Fu 2019; Mansor & Ismail 2012; Purwar & Scott 2019).

Exemplar studies. The exemplar studies in this theme were chosen to emphasize the different types of feedback used in engineering education research, specifically text-based, interactive, and automated feedback. Rutz and Ehrlich (2016) used the Community of Inquiry (COI) framework to evaluate the use of text-based and interactive feedback on learner engagement in an online course on effectiveness in technical organizations. Learners were offered both conventional text-based feedback and interactive feedback on assignments, and surveys with five-point Likert scales and open-ended questions were used to collect student perceptions on both types of feedback and to assess the impact of these feedback formats on student perceptions related to different parts of the COI framework (cognitive presence, social presence, teacher presence). The responses showed that both formats for receiving instructor feedback helped students feel connected to the course. However, students rated their perceptions of the three elements related to the COI framework as higher for the interactive feedback than for the text-based feedback.

In another study, Sancho-Vinuesa et al. (2018) presented the use of a quiz-based assessment tool with automatic feedback in two mathematics courses for computer and telecommunications engineering students. The tool provides random self-evaluation exercises, such as multiple-choice, true or false, matching, and short answer questions, to assess student learning. The results revealed that students’ learning of mathematical analysis concepts, engagement, and completion rates increased from previous semesters with the adoption of the new application.

Research implications. Further research is necessary into the different types of feedback that can enhance student learning and performance in an online engineering course. Additionally, work is needed to investigate further the impact of feedback on student learning in an online engineering course. Some potential questions tying these areas together are how students perceive automated feedback as compared to the feedback they receive from the instructor, how the quality of automated feedback compares to the feedback they receive from the instructor, and how the differences between automated and instructor feedback affect student learning (Sancho-Vinuesa et al. 2018). This question is important because students in online classes already have limited interactions with their instructors, and automating feedback results in even more of a loss in the possibility of obtaining personalized feedback. Separately, while collecting student feedback at the end of a course is common (Fu 2019; Mansor & Ismail 2012; Purwar & Scott 2019), these evaluations typically focus on course outcome attainment or student perceptions. Determining strategies to effectively collect meaningful data as a part of student course evaluations represents another future research area that would support continuous improvement in online course delivery.

Practice implications. Instructors in the online education space should provide different types of feedback to students, including text-based feedback, interactive feedback, and automated feedback (Rutz & Ehrlich 2016; Sancho-Vinuesa et al. 2018). Instructors can use different feedback techniques to determine which types work best for their course, students, and themselves. Further, instructors can collect student feedback during and near the end of the course to help them reflect on their course design and delivery and look for opportunities to make improvements to their course in subsequent offerings (Fu 2019; Mansor & Ismail 2012; Purwar & Scott 2019).

Theme 5: Challenges in online engineering

We categorized nine articles under challenges in delivering online engineering courses. Three out of the nine articles were focused primarily on challenges inherent to online engineering courses (Hachey et al. 2015; Perales Jarillo et al. 2019; Pedrosa et al. 2020), such as comparatively lower student sense of belongingness and higher student feelings of isolation and attrition relative to face-to-face courses (Bowers & Kumar 2015; Gregori et al. 2018; Robertson 2020; Shea & Bidjerano 2016).

The remainder of the articles under this theme documented various challenges faced by online instructors, including challenges using the course learning management system (LMS), challenges designing online laboratories and design projects, challenges maintaining student engagement and student-faculty interactions, challenges equipping students with e-learning skills, challenges closing the gender gap in online engineering graduate enrollments, challenges providing timely feedback, challenges providing clear enough instruction, and technical challenges (Cooper et al. 2020; Hammout & Hosseini 2020; Kiridena et al. 2014; Levy & Ramim 2017; Rutz & Ehrlich 2016; Zhang 2020).

Exemplar studies. We chose the exemplar studies for this theme because detailing challenges in online engineering was an important aspect of the articles’ contribution. Pedrosa and colleagues (2020) identified the pedagogical and technical challenges that arose from implementing SimProgramming in an online software development laboratory for informatics engineering students. SimProgramming is a motivation-based instructional approach that teaches students programming through a dynamic design, development, testing, and analysis process. The identified challenges included low and late student participation, student sense of isolation, inadequate feedback mechanisms, and unclear task descriptions, primarily attributable to ineffective instructor and student communication.

In another exemplar study, Hachey, Wladis, and Conway (2015) investigated the impact of student performance and prior online course experiences on the successful completion of future online courses. This study used logistic regression to analyze data from 1,566 community college students enrolled in online STEM courses. The study revealed that students’ prior online experience significantly predicted the outcome of subsequent online courses, controlling for grade point average (GPA). Specifically, students who were unsuccessful in completing prior online courses and had lower GPAs were at higher risk than other students of dropping out of or failing subsequent online courses.

Research implications. Pedrosa et al. (2020) reported that students struggle with feelings of isolation, low sense of belongingness, low participation, and misunderstanding of task-related descriptions in their online courses. Research is required to explore these challenges in online engineering education and their influencing factors. Additionally, Hachey et al. (2015) found that students’ previous unsuccessful experiences in online courses and low GPAs can increase their likelihood of dropping out of future online courses. Therefore, investigating the types of interventions and strategies that mitigate these influences represents another potential direction for future research.

Practice implications. Ensuring that all course information (e.g., instructions, resources, deadlines) is clear to students is essential for maintaining student interest, engagement, and retention (Pedrosa et al. 2020). Hence, instructors must devote sufficient time to examining the material offered to students and confirming that the information is easily understood. Further, online instructors should get to know their students and monitor their progress in the course for clues that they are at risk of dropping out of the course (Hachey et al. 2015).