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Cognitive Load and Online Course Quality: Insights from Instructional Designers in a Higher Education Context

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Title: Cognitive Load and Online Course Quality: Insights from Instructional Designers in a Higher Education Context
Language: English
Authors: Caskurlu, Secil (ORCID 0000-0001-8716-9586), Richardson, Jennifer C. (ORCID 0000-0001-7534-1406), Alamri, Hamdan A. (ORCID 0000-0001-8350-4383), Chartier, Katherine, Farmer, Tadd (ORCID 0000-0003-2549-1232), Janakiraman, Shamila (ORCID 0000-0001-8750-3601), Strait, Marquetta, Yang, Mohan (ORCID 0000-0003-0856-0814)
Source: British Journal of Educational Technology. Mar 2021 52(2):584-605.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 22
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Instructional Design, Educational Quality, Cognitive Processes, Difficulty Level, Higher Education, Standards
DOI: 10.1111/bjet.13043
ISSN: 0007-1013
Abstract: This multiple case study investigates instructional designers' perceptions of online course quality, their use of cognitive load strategies when designing online courses, and whether utilization of these strategies contribute to online course quality. The participants of this study were instructional designers (n = 5) who worked in various campus programs at a large Midwestern university. Data sources included pre-interview survey, semi-structured interview and sample course design documents. Employing a pattern matching technique, the results showed that instructional designers (a) define online course quality based on established standards and rubrics; (b) apply cognitive load strategies intuitively while designing online courses; and (c) consider CLT design strategies as an element contributing to course quality. The results also showed instructional designers' use of cognitive load strategies mainly focused on reducing extraneous cognitive load. Implications for practice and research as well as directions for future research are discussed.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1286586
Database: ERIC
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  Value: <anid>AN0148800735;58i01mar.21;2021Feb20.01:20;v2.2.500</anid> <title id="AN0148800735-1">Cognitive load and online course quality: Insights from instructional designers in a higher education context </title> <p>This multiple case study investigates instructional designers' perceptions of online course quality, their use of cognitive load strategies when designing online courses, and whether utilization of these strategies contribute to online course quality. The participants of this study were instructional designers (n = 5) who worked in various campus programs at a large Midwestern university. Data sources included pre‐interview survey, semi‐structured interview and sample course design documents. Employing a pattern matching technique, the results showed that instructional designers (a) define online course quality based on established standards and rubrics; (b) apply cognitive load strategies intuitively while designing online courses; and (c) consider CLT design strategies as an element contributing to course quality. The results also showed instructional designers' use of cognitive load strategies mainly focused on reducing extraneous cognitive load. Implications for practice and research as well as directions for future research are discussed. Practitioner NotesWhat is already known about this topic Cognitive load theory (CLT) provides empirically tested strategies to manage cognitive load in different settingsCLT strategies has a positive impact on student learning processes and outcomesWhen designing online courses, it is important for faculty to collaborate with instructional designers to manage cognitive load and improve online course qualityThere is a need to investigate instructional designers' perceptions of online course quality and their use of CLT strategies when designing online coursesWhat this paper adds Provides a deep understanding of instructional designers perspectives on online course quality, application of CLT strategies while designing online courses and how application of these strategies contribute to the online course qualityAlthough instructional designers identify multiple CLT strategies from their work and perceive CLT strategies as an element contributing to course quality, they apply these strategies innatelyImplications for practice and/or policy Multiple stakeholders should be involved in determining online course qualityCollaboration between faculty and instructional designers is essential to manage cognitive load and increase online course qualityCLT and related theories should be emphasized in instructional design programsFuture research should focus on how instructional designers integrate CLT strategies into the systematic instructional design process and instructional designers' decision‐making process through think‐aloud and/or journaling efforts</p> <p>Keywords: online learning; cognitive load; cognitive load theory; instructional design; instructional designer; case study</p> <hd id="AN0148800735-2">Introduction</hd> <p>Online education has become an attainable and appealing option for diverse learners pursuing their education while leading busy lives (Seaman, Allen, & Seaman, 2018). Given this, institutions are continually seeking ways to enhance the quality of online courses to increase student satisfaction, enrollment and retention (Legon & Garrett, 2017). Previous literature has provided a variety of standards aiming to improve course design thereby increasing student satisfaction and learning (eg, Baldwin, Ching, & Hsu, 2018; Esfijani, 2018). Cognitive Load Theory (CLT) is an instructional design theory that focuses on knowledge acquisition and provides research‐based strategies to enhance student learning experiences and course quality (eg, Rodchua, 2007; Sweller, 2020; Sweller, van Merriënboer, & Pass, 2019). Instructional designers are often considered key stakeholders to support faculty in managing cognitive load (CL) and designing quality online courses (eg, Brigance, 2011; Sentz, Stefaniak, Baaki, & Eckhoff, 2019). Yet, instructional designers' perceptions of online course quality and how they apply CLT strategies when designing quality online courses is lacking. Therefore, this multiple case study aims to investigate instructional designers' perceptions of online course quality, their use of CLT strategies when designing online courses and whether deployment of these strategies contributes to online course quality.</p> <hd id="AN0148800735-3">Literature review</hd> <p></p> <hd id="AN0148800735-4">Cognitive load theory</hd> <p>CLT is an instructional design theory that considers "working memory constraints as determinants of instructional design effectiveness" (Sweller <emph>et al</emph>., 1998, p. 251). Working memory, also known as short‐term memory, organizes and manipulates information into new or existing schemas that can be encoded into long‐term memory (Sweller, van Merriënboer, & Paas, 1998). Working memory has a limited capacity of holding about seven items or elements of information at a time (Sweller <emph>et al</emph>., 1998) whereas, long‐term memory has unlimited capacity and stores information in the form of schemas that link together multiple pieces of related information into a single unit (Clarke, Ayres, & Sweller, 2005). When a schema is recalled into working memory, it is processed as a single unit of information (Clarke <emph>et al</emph>., 2005), enabling the learner to use his or her limited cognitive resources to process more novel information (Kalyuga, 2009; Kirschner, 2002). Schemas can be automated as a result of extensive practice (Sweller <emph>et al</emph>., 1998) allowing "free working memory capacity for other activities because an automated schema, acting as a central executive, directly steers behavior without needing to process it in working memory" (van Merriënboer & Sweller, 2010, p. 87). In the absence of the availability of these schemas in the learning environment, a learner is left to use resource‐intensive general search and processing strategies to process information (Kalyuga, 2007, 2009; Sweller, 1988), sometimes resulting in cognitive overload.</p> <p>CLT is interested in reducing "unnecessary informational complexity and so reduce working memory load when acquiring new knowledge" (Sweller, 2020, p. 8). Accordingly, information complexity "can be attributed to either differences in intrinsic or extraneous cognitive load" (Sweller, 2020, p. 9). Intrinsic cognitive load (ICL) is related to the intrinsic nature of the material (eg, subject matter difficulty, element interactivity) processed by working memory and student's prior knowledge of the subject (de Jong, 2010; Sweller <emph>et al</emph>., 1998). ICL "can only be altered by instructional interventions when the task to be learned is altered (eg, simplification) or by the act of learning itself" (van Merriënboer & Sweller, 2010, p. 87). On the contrary, extraneous cognitive load (ECL) is related to the "manner in which the material is presented, or the activities required of students" (Sweller <emph>et al</emph>., 1998, p. 259). ECL does not directly contribute to learning so one purpose of instructional design is to reduce the ECL caused by instruction (de Jong, 2010).</p> <hd id="AN0148800735-5">CLT and instructional design</hd> <p>CLT aims to guide instructional design practices to (a) consider students' differences and cognitive abilities prevent cognitive overload (eg, Hattie & Yates, 2014; van Merriënboer, Kirschner, & Kester, 2003); (b) deal with highly complex learning tasks; and (c) assist students to develop expertise (van Merriënboer & Sweller, 2010). To accomplish these, CLT provides empirically based instructional design strategies (see Appendix A) to manage ICL by freeing up cognitive resources and reducing ECL by devoting those resources to the construction or manipulation of long‐term schema (Clarke <emph>et al</emph>., 2005; Sweller, Ayres, & Kalyuga, 2011). Note that selection of the strategies should consider learner's prior knowledge and expertise (Clarke <emph>et al</emph>., 2005) as one strategy could effectively reduce CL for novice learners but may not be ideal for expert learners (Kalyuga, 2009).</p> <p>To date, an extensive body of research has shown the effectiveness of CLT strategies on student learning processes and outcomes through pre/post‐experimental studies (eg, Clarke <emph>et al</emph>., 2005; Sweller & Chandler, 1994) in different formats including e‐learning (Sweller, 2020). Previous research has also addressed the need for collaboration between faculty and instructional designers while designing online courses to manage CL and improve online course quality (Brigance, 2011) as instructional designers' theoretical and pedagogical knowledge and skills in course design, implementation and evaluation could provide valuable expertise (Scoppio & Luty, 2017). In other words, such collaborations would necessitate the involvement of instructional designers as key stakeholders and experts who are knowledgeable and skilled at understanding and designing for quality online learning experiences while managing complex, instructional design tasks. However, when examining online course quality, instructional designers' perspectives are often not considered (Esfijani, 2018). Furthermore, research exploring if and how instructional designers apply CLT strategies when designing quality online courses is lacking. Therefore, this study investigated instructional designers' perceptions of online course quality and their use of CLT strategies when designing online courses. Specifically, our research questions were:</p> <p></p> <ulist> <item> How do instructional designers define a quality online course?</item> <p></p> <item> How do instructional designers identify CLT design strategies in the online courses they design?</item> <p></p> <item> What are the perceptions of instructional designers on how CLT design strategies contributing to course quality?</item> </ulist> <hd id="AN0148800735-6">Methods</hd> <p>Given our goal, this study was guided by Yin's case study methodology, which "investigates a contemporary phenomenon in depth and within its real world context" (Yin, 2014, p. 16). A case is defined as an individual instructional designer in the current study. One challenge evidenced in case study research is how to manage data analysis which necessitates the use of an analytic strategy (Yin, 2014). To address this challenge, our analytic strategy used CLT as a theoretical framework to inform the instrument development (eg, pre‐survey, interview protocol) and data analysis. In reviewing relevant literature (eg, Hattie & Yates, 2014; Moreno & Mayer, 2007; van Merriënboer & Sweller, 2010), strategies to manage CL were identified and used to create the <emph>CL Principle Guide</emph> for our study and participants (see Appendix A).</p> <hd id="AN0148800735-7">Context and participants</hd> <p>Participants consisted of five instructional designers at a large, Midwestern university who worked in various campus programs (see Table 1). Participants' instructional design experience ranged from 3 to 10 years, with some designers also possessing previous teaching experience (eg, K‐12, higher education).</p> <p>1  TableParticipant demographics</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Pseudonym</th><th align="left">Degree</th><th align="left">Years of experience</th></tr></thead><tbody><tr><td align="left">Ann</td><td align="left">M.S. Educational Technology/Instructional Design</td><td align="left">10+ Instructional Design</td></tr><tr><td align="left">10+ Teaching</td></tr><tr><td align="left">Erin</td><td align="left">M.S. Educational Technology/Instructional Design</td><td align="left">10+ Instructional Design</td></tr><tr><td align="left">Nick</td><td align="left">M.S. Non‐Educational Technology/Instructional Design</td><td align="left">6+ Instructional Design</td></tr><tr><td align="left">Ph.D. Non‐Educational Technology/Instructional Design</td><td align="left">5+ Teaching</td></tr><tr><td align="left">Additional training in Instructional Design</td></tr><tr><td align="left">Rian</td><td align="left">M.S. Educational Technology/Instructional Design</td><td align="left">6+ Instructional Design</td></tr><tr><td align="left">5+ Teaching</td></tr><tr><td align="left">Trisha</td><td align="left">M.S. Non‐Educational Technology/Instructional Design</td><td align="left">3+ Instructional Design</td></tr><tr><td align="left">10+ Teaching</td></tr></tbody></table> </ephtml> </p> <hd id="AN0148800735-8">Data collection</hd> <p>We collected two primary sources of data: pre‐surveys and semi‐structured interviews (see Appendix B for the interview protocol) and one secondary source; sample course design documents. Pre‐survey and interview protocols were based on the literature, pilot tested with two instructional designers and revised to incorporate feedback. The pre‐survey data collected background and demographic data needed to understand our participants and build rapport; it also served as a data triangulation point by asking participants to indicate which CLT strategies they were currently using from the list presented in the <emph>CL Principle Guide</emph> (Yin, 2014). Finally, design documents and verbal examples further illuminated participants' experiences and understandings as examples of quality and CLT strategies in existing course designs and have contributed to the descriptive aspects of the study.</p> <p>Participants were emailed a pre‐survey and provided pre‐interview materials (eg, <emph>CL Principle Guide,</emph> Appendix A<emph>;</emph> interview protocol, Appendix B). Semi‐structured interviews were conducted by two investigators and lasted approximately 60–90 minutes. Participants were asked during the interview to provide sample course design documents. Interviews were recorded, transcribed verbatim, de‐identified and subsequently uploaded to NVivo for analysis.</p> <hd id="AN0148800735-9">Data analysis</hd> <p>Initial analyses focused on developing case descriptions for the individual participants using both the pre‐interview surveys and interview data. Next, pattern matching was used to analyze data by determining whether propositions derived from the literature matched our participants' experiences (Almutairi, Gardner, & McCarthy, 2014; Yin, 2014). Pattern matching as a technique allows researchers to link data to theoretical propositions obtained from prior research, knowledge or theory (Yin, 2014). Almutairi <emph>et al</emph>. (2014) suggested three steps which guided our pattern matching analysis: (a) stating the study's proposition(s); (b) testing the empirically based proposition from each case against the predicted one; and (c) providing theoretical explanations and developing research outcomes. To note that our aim was not to confirm or dispute the propositions but rather to build explanations about how our propositions did, or did not match, our participants' experiences (Yin, 2014).</p> <p>Specific coding procedures were informed by Saldana (2016) and included three distinct coding cycles: (<reflink idref="bib1" id="ref1">1</reflink>) Initial reading of transcripts and inductive coding to identify the preliminary codes aligning with the CLT strategies identified by previous research; (<reflink idref="bib2" id="ref2">2</reflink>) Creation of additional codes, or relevant sub‐codes; and (<reflink idref="bib3" id="ref3">3</reflink>) Finalizing the coding scheme (see Appendix A). Through an iterative process, two researchers independently coded and compared two transcripts to establish consensus and consistency. The disagreements in coding were discussed with an additional researcher. Final inter‐coder reliability was established at 96.33%, indicating good inter‐coder reliability (Creswell, 2014). The remaining interview transcripts were independently coded by the same researchers with two additional researchers "spotcoding" for consistency.</p> <hd id="AN0148800735-10">Data trustworthiness</hd> <p>As a team of researchers and instructional designers, we share similar educational backgrounds and experience as our participants. This shared background and experience provides credibility between inquirer and participant and allows us to accurately represent participant's views and experiences (Yin, 2014). We believe quality course design matters for multiple stakeholders (eg, administrators, instructors, students) and has far reaching implications (eg, student performance, motivation, retention).</p> <p>To establish trustworthiness, we incorporated several procedures as promoted by Lincoln and Guba (1985). First, credibility was established through data triangulation and peer debriefing. Multiple sources of data were triangulated from five participants to utilize the strengths and minimize possible bias inherent in each data source (Yin, 2014). Peer debriefing meetings were held regularly with the research team to increase awareness of possible bias in our data collection and analysis, ensure data were coded appropriately and avoid making unwarranted assumptions. Additionally, thick descriptions of the research context, the participants and their work were sought from each participant to assist in the transferability of our findings to similar contexts. Second, to establish dependability, or reliability during data analysis, multiple researchers were used as described above. Third, confirmability was established by ensuring research protocols were based in the literature, pilot tested with experienced instructional designers to establish face and content validity and understood by participants. Results of pilot testing revealed that our use of CLT terms were difficult to understand and negatively impacted the ability of the instructional designers to provide in‐depth responses and relevant examples. Survey items and interview protocols were adapted to contain verbiage more familiar to instructional designers.</p> <hd id="AN0148800735-11">Results</hd> <p>This section presents five case studies followed by an examination of whether observed propositions match the predicted propositions for each case (Almutairi <emph>et al</emph>., 2014). The predicted and literature‐based propositions for our study were:</p> <p></p> <ulist> <item> Instructional designers define quality in online courses based on established rubrics and standards.</item> <p></p> <item> Instructional designers identify multiple CLT design strategies from their work in developing online courses.</item> <p></p> <item> Instructional designers perceive CLT design strategies as an element contributing to online course quality.</item> </ulist> <hd id="AN0148800735-12">Case 1: Ann</hd> <p></p> <hd id="AN0148800735-13">Quality in online courses</hd> <p>Essential elements of quality online course design for Ann start with providing students support, including a sense of community and a feeling of trust to engage students in active participation, collaboration and interaction in an online environment. A co‐dependent community, in her view, contributes to a sense of accountability with students providing peer responses and feedback and allows students to believe that it is okay to take risks and possibly fail:</p> <p>People can't really grow unless they're willing to be vulnerable and in order to have that vulnerability, you have to feel like people are on your side and that they're not going to make fun of you... and that they're there really to just be a support.</p> <p>Ann indicated she uses established rubrics (eg, Quality Matters, university‐generated rubric) and templates to guide her design. Additionally, she indicated that she utilizes peer instructional designers to review her courses and revisits courses as part of an iterative development process.</p> <hd id="AN0148800735-14">Identification of CLT design strategies in online course design</hd> <p>Ann's definition of CL demonstrated her understanding of the concept; "there is a certain amount of information that an individual can absorb at one point in time and we have to be cognizant of that limitation and only give them as much information as they can handle at one time." However, while she was generally familiar with CLT strategies, she referred to them by other names based on her background and training (eg, "segmenting" versus "chunking").</p> <p>According to pre‐survey, she applied all 13 CLT strategies identified for this study. During the interview, Ann relayed examples of modality, multimedia, split‐attention, segmenting, sequencing, goal‐free, self‐explanation, worked‐example, cueing, redundancy and feedback strategies. She also explained her use of the terms was based on her understanding from the definitions and examples in the <emph>CL Principle Guide</emph> provided prior to the interview. This led to some misinterpretations in her identification of strategies, including a sequencing example she mistook as an example of segmenting.</p> <p>Ann also provided additional strategies, like pre‐assessment, that were not included in the <emph>CL Principle Guide</emph>, as she talked about the importance of adapting design strategies to manage CL. For example, she indicated that the goal‐free principle was especially effective for gifted learners and higher level learners but might cause overload for other learners. Thus, pre‐assessing students was a strategy she used regularly. While there is no specific pre‐assessment CLT strategy, prior knowledge plays a foundational role in many CLT strategies (eg, multimedia principle, self‐explanation principle and pre‐training principle). It is apparent from Ann's interview and examples that she intuitively integrates CLT strategies into her course design.</p> <hd id="AN0148800735-15">Cognitive load and online course quality</hd> <p>When asked about CLT strategies and online course quality, Ann suggested that CL should not be "the only consideration," however, quality would not be accomplished "if you have an overload of information like... 30 hours of video" since students would likely not achieve positive outcomes. Several times, she indicated the only real way to know if the load is balanced is to get direct student feedback; yet, this is a challenge for instructional designers since they often lack access to students. Overall, Ann tacitly perceives CLT strategies as influencing the online course quality, without explicitly considering them until this interview.</p> <hd id="AN0148800735-16">Case 2: Erin</hd> <p></p> <hd id="AN0148800735-17">Online course quality</hd> <p>Erin follows Quality Matters which provides guidelines to know what to look for quality. For example, she reported a better understanding of and desire to make her courses "welcoming" to students. Moreover, she felt that quality courses provided opportunities for application‐oriented or relevant projects so that students "can see [the concept] in action."</p> <p>Moreover, a lot of Erin's design work involved asking herself basic questions from a learner's perspective: "does it make sense, does it flow, is it reasonable in terms of the users' time commitments?". Given her particular target audience, she feels it is important to make sure the courses are standardized and "as organized, as concise as possible."</p> <p>In addition to using established rubrics, Erin also indicated that she reviews student course evaluations after a course's initial run and revisits the courses to make needed modifications.</p> <hd id="AN0148800735-18">Identification of CLT design strategies in online course design</hd> <p>Erin demonstrated her understanding of CLT when she defined it: "how much brain power you need to focus in and learn the materials." She clarified that her experiences with CLT strategies are based on experience rather than formal training and using them is more intuitive than purposeful for her.</p> <p>Erin's pre‐survey and interview data showed several examples of how she balanced CL for learners. For example, Erin showed a PowerPoint presentation that included narration as an example of the modality principle. As for the multimedia principle, she showed a printable colored graphic or a job aid to help learners remember a complex equation taught during a lecture. Erin built sequencing into a course by breaking a large final project into elements completed in multiple steps. She helped faculty create "how‐to" files including screen captures of calculations on a spreadsheet that served as worked‐examples. While designing quizzes, Erin ensured that multiple trials and instant feedback were provided. For one example she misinterpreted the variability principle, by basing it on the use of multiple ("various") resources instead of variability within a given task. Likewise, she explained her use of the cueing principle through the inclusion of instructor‐created tips highlighting the course organization, although she described it as the pre‐training principle. Based on student needs and characteristics, Erin described additional strategies she felt helped manage CL, including using small groups to facilitate discussions and limiting collaborative activities for working professionals.</p> <hd id="AN0148800735-19">Cognitive load and online course quality</hd> <p>Erin explained that while she recalls learning about CL formally at college, it was not something she considered directly in her work. She was generally aware of the causes of cognitive overload and intuitively knew how to reduce CL to design quality online courses. She admitted, "It was interesting to think, to realize, 'yeah, I'm actually doing some of these things.'" In the end she said "...it seems like being aware of these different strategies, it will enhance maybe what I can help suggest to faculty. And so, I think that we end up with a better‐quality product." In sum, Erin saw how CLT strategies could impact the quality of online courses, although this was not an aspect she considered directly prior to this study.</p> <hd id="AN0148800735-20">Case 3: Nick</hd> <p></p> <hd id="AN0148800735-21">Quality in online courses</hd> <p>For Nick, his insights about course design come from his own experience as an instructor and his instructional design training. He used established rubrics (eg, QM rubrics, OLC scorecard, university‐generated rubric) to guide his work as they provided feedback about key course assignments and course quality. As he explained:</p> <p>there needs to be a mix of audio and visual instruction, and that having videos is important (particularly a few videos that have the professor visible). Further, I think it is important to use discussion boards in some fashion, to connect students with each other and with the professors.</p> <p>He believed a quality online course both challenges learners to learn and provides easy access to all content following a uniform layout. Nick opined that low attrition and low dropout rates are signs of a quality online course.</p> <hd id="AN0148800735-22">Identification of CLT design strategies in online course design</hd> <p>Nick explained that his CLT knowledge came from hands‐on experience while teaching and designing his own online courses and that he also benefited from other instructional designers' knowledge.</p> <p>Nick favored adapting course design to audience needs and suggested segmenting videos into short chunks and reducing the number of readings. When creating videos, he sought to improve the course flow using the segmenting and sequencing principles by including a separate objective for each video segment. Garnering learners' attention was also an important instructional strategy. Nick explained, "the best way to avoid cognitive overload, even in the sciences, is to somehow create an engaging narrative." While not a recognized CLT strategy, this example does share some aspects with the personalization principle (see Mayer, 2014). When discussing assessments, Nick emphasized the use of low stakes quizzes that provided immediate feedback. He said: "As far as cognitive load, I avoided, as much as I could, high stakes, summative assessments." The quizzing or testing effect can be used as a learning tool to correct errors and to remember better, he added. He created different rubrics to assess short and long writings and he used them to outline learner expectations and to provide feedback on analytical writing. While not identified as a CLT strategy to‐date, the quizzing effect is a related strategy from the cognitive sciences dealing with repetition and long‐term retention (Roediger, & Butler, 2011; Roediger, & Karpicke, 2006). These examples show Nick's understanding of both CLT design strategies and some related design strategies.</p> <hd id="AN0148800735-23">Cognitive load and online course quality</hd> <p>Nick felt that learning from online courses can be more difficult, owing to more distractions (eg, family, work) combined with more learner control. He indicated that systematic course design could help minimize CL for learners and explained that his use of CLT strategies was fully intertwined with his instructional design process. As Nick explained, his use of backward design</p> <p>... is the best first way to avoid cognitive overload, face‐to‐face or online...And so for me, scaffolding and backward design is the way to get there. Is the first step. And then going into these more complex things of segmenting the videos, using formative assessments rather than summative assessments.</p> <p>Nick's example demonstrated his belief in the role of CLT design strategies as an element contributing to quality online courses.</p> <hd id="AN0148800735-24">Case 4: Rian</hd> <p></p> <hd id="AN0148800735-25">Quality in online courses</hd> <p>Considering online course quality, Rian emphasized the importance of course organization embedded in a student‐centered design that "allows students to know exactly what they will be doing." He used established rubrics (QM rubrics, OLC scorecard, university‐generated rubric) to guide his design "make sure that, from a student perspective, they know how to find the content that they need. Their expectations are clear in exactly what they should be doing...there is alignment between outcomes and assessments." However, he did not feel that rubrics were enough to ensure quality. Using his own experiences, he also examined the courses from the learners' perspective:</p> <p>[Once] most of the course is built, we start to look at it holistically... Okay, what's missing? Is this going to get students where I want them to go? Is this enough? And at that point, we can kind of start to circle back and say, Where are there gaps? Where can we actually push the students a little bit more? Where could I give them a little bit more experience in engaging with this content?.</p> <hd id="AN0148800735-26">Identification of CLT design strategies in online course design</hd> <p>The pre‐survey revealed that Rian used the majority of the CLT strategies in his course designs. He described CL as:</p> <p>maintaining that ability for students to take in information, and take it in at a pace and a level which gives them that opportunity to both take it in, reflect upon it, and actually do something with it before inputting more information and complicating that process even further.</p> <p>Rian also emphasized the need for courses to be navigable so that students can find what they need when needed. For him, effective navigation can be achieved by using the cueing strategy. Using a sample course, Rian demonstrated his use of short overview videos at the beginning of each module thereby explaining target learning outcomes and what learners should focus on. These videos were of the instructor and exemplified the personalization strategy. Similarly, chunking or segmenting, was another common strategy he used to help faculty format their course content.</p> <p>Rian also stressed importance of not to get stuck in "that straight up week one, week two, week three" format, but to make sure students see the connection between the weekly content and the big picture.</p> <p>Rian noted the need to adapt or differentiate content delivery based on levels and disciplines. For example, he discussed how introductory or gateway courses tend to be content‐heavy while advanced courses are generally more focused on knowledge application. While the CLT strategies we examined do not include instructional differentiation, this does relate to the "expertise reversal principle" a concept involving fading and scaffolding as learners gain more experience (see Kalyuga, 2014; Kalyuga, Ayres, Chandler, & Sweller, 2003).</p> <p>Much of Rian's design was driven by his education in instructional design, his own learning experiences and course design rubrics like QM and FutureLearn. To balance CL, he intentionally implemented CLT strategies.</p> <hd id="AN0148800735-27">Cognitive load and online course quality</hd> <p>Rian modifies course elements based on students' feedback to enhance course quality. To balance CL for learners and to improve course quality, he advised that instructors and designers should think from learners' perspectives when designing a course. Rian noted that CLT strategies alone may not improve quality; quality courses include level‐appropriate content, visually appealing layout and intuitive navigation. Rian recognizes the ramifications of cognitive overload on student learning.</p> <hd id="AN0148800735-28">Case 5: Trisha</hd> <p></p> <hd id="AN0148800735-29">Quality in online courses</hd> <p>Trisha's many years of experience as a public educator and instructional designer provided her with insights about quality course design. Her perspectives were strongly influenced by established course rubrics. To her, quality online courses should have "clearly defined" and "measurable objectives," achievable outcomes and relevant course content for students to master. Noting that each student's path through a course may vary, Trisha explained: "Quality courses have more than one path. They have the opportunity for students to revisit as necessary for mastery, and to reduce anxiety as they try to reach mastery." She also favored using brief or low‐stake quizzes, self‐reflection, regular and substantive feedback from instructors, and interactions among learners. Notably, Trisha perceives universal design principles as necessary, as accessibility is a component of a quality online course. Finally, Trisha indicated that she usually referred to quality rubrics during her course design and development, (eg, QM rubrics, university‐generated rubric) and had peer‐reviews conducted on her courses.</p> <hd id="AN0148800735-30">Identification of CLT design strategies in online course design</hd> <p>Though the CLT terminology was not frequently utilized in Trisha's interview, her examples and explanations demonstrated her use of strategies to manage learners CL. For example, content was chunked into the learning modules and objectives by weeks, which were then sequenced from simple to complex and "looped" back to the learners. As Trisha explained, "looping" would be in line with the sequencing strategy:</p> <p>Starting with introduction to concepts, and going back over them several times in the term, each revisit being further in depth and more complex, so learners interact with a concept at least three times in progressively more difficult and detailed situations.</p> <p>According to her, looping should be integrated in conjunction with sequencing so that learners have the opportunity to revisit the content to ensure mastery.</p> <p>Trisha spoke of using media in the courses she designed, the most common of which included PowerPoint mixed with video. These videos may include the physical appearance of the instructor or a PowerPoint with voiceover, each of which would be captioned.</p> <p>For the pre‐training strategy, Trisha showed a literature course where vocabulary, character names, relationships and the cast of characters were provided in advance of activities. As with other participants who had a K‐12 background, Trisha adapted her design based on students' level and discipline, and discussed the necessity of making the design flexible with multiple approaches available. Trisha revealed her intuitive implementation of CLT strategies, basing her use of them on knowledge of "what works best for students" gained from her experiences in K‐12 education, instructional design and her online teaching experience.</p> <hd id="AN0148800735-31">Cognitive load and online course quality</hd> <p>When asked about her perception of the connection between CL and online course quality, Trisha said:</p> <p>Everything is about clarity, and understanding, and simplification with appropriate redundancy without beating a dead horse. I think if we keep that in mind, both in design and delivery, that we'll achieve more of what we aim to with student understanding and faculty satisfaction with the outcomes of the courses.</p> <p>According to Trisha, taking CL into consideration in course design and delivery can move us closer to accomplishing quality courses.</p> <hd id="AN0148800735-32">Discussion</hd> <p>Overall, results showed that all three predicted patterns matched the observed patterns across five cases. This section provides a comparison across these cases, thus, allowing for explanations and justifications of the findings in accordance with previous research.</p> <hd id="AN0148800735-33">Proposition 1: Quality in online courses</hd> <p>While not all participants (ie, Ann, Erin and Nick) provided an explicit definition of online course quality they all provided insights into the characteristics of a quality online course. When asked about how they ensured online course quality, four participants indicated they always used established rubrics (eg, QM, OLC scorecard and university‐generated rubric) to guide their design. Beyond the established rubrics, each participant discussed seeking different perspectives (eg, peer reviews, course evaluations), which also aligns with recommendations from previous research (Esfijani, 2018; Sun, Tsai, Finger, Chen, & Yeh, 2008). Each also discussed how they were influenced by their personal experiences as learners, instructors and designers. Additionally, a student‐centered design to provide satisfying learning experiences was common to all participants.</p> <p>Moreover, the participants highlighted that a quality online course not only depends on instructional design, but also how instructors facilitate the course. Corresponding with previous research, the present results stress that faculty‐instructional designer collaboration is essential to promote online course quality (Richardson <emph>et al</emph>., 2019; Brigance, 2011).</p> <hd id="AN0148800735-34">Proposition 2: Identification of CLT strategies in online course design</hd> <p>The results showed that all participants applied CLT strategies; however, most did so intuitively (<emph>n</emph> = 4). Aligning with the pre‐survey, the participants identified various CLT strategies in their work during the interview. Table 2 summarizes the identified CLT strategies from each participant.</p> <p>2 TableIdentified CLT strategies</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Case</th><th align="left">ICL</th><th align="left">ECL</th></tr></thead><tbody><tr><td align="left">Ann</td><td align="left"><list list-type="Bullet"><list-item><p>Sequencing</p></list-item><list-item><p>Self‐explanation</p></list-item></list></td><td align="left"><list list-type="Bullet"><list-item><p>Modality</p></list-item><list-item><p>Multimedia</p></list-item><list-item><p>Split attention</p></list-item><list-item><p>Segmenting</p></list-item><list-item><p>Goal‐free</p></list-item><list-item><p>Worked‐example, cueing</p></list-item><list-item><p>Redundancy</p></list-item><list-item><p>Feedback strategies</p></list-item></list></td></tr><tr><td align="left">Erin</td><td align="left">Sequencing</td><td align="left"><list list-type="Bullet"><list-item><p>Multimedia</p></list-item><list-item><p>Worked examples</p></list-item></list></td></tr><tr><td align="left">Nick</td><td align="left">Sequencing</td><td align="left"><list list-type="Bullet"><list-item><p>Segmenting</p></list-item><list-item><p>Cueing</p></list-item><list-item><p>Feedback</p></list-item></list></td></tr><tr><td align="left">Rian</td><td align="left">–</td><td align="left"><list list-type="Bullet"><list-item><p>Cueing</p></list-item><list-item><p>Chunking/segmenting</p></list-item><list-item><p>Redundancy</p></list-item></list></td></tr><tr><td align="left">Trisha</td><td align="left">Sequencing</td><td align="left"><list list-type="Bullet"><list-item><p>chunking/segmenting</p></list-item><list-item><p>Multimedia</p></list-item><list-item><p>Pre‐training</p></list-item></list></td></tr></tbody></table> </ephtml> </p> <p>Even though all participants provided a clear description of CL and identified strategies in their work, several misunderstandings or misinterpretations in defining or demonstrating the CLT strategies occurred (eg, sequencing, variability). This could be tied to the lack of formal training in CLT; most designers said that they had not been trained in CLT or related theories. Yet, they were aware of the need to "manage" CL even if they did not understand it in those terms.</p> <p>For the identified strategies, in alignment with previous research, the participants mainly focused on reducing ECL (eg, Sentz <emph>et al</emph>., 2019), which is directly influenced by instructional design and procedures (Sweller, 2020; Wong, Leahy, Marcus, & Sweller, 2012). Results also showed that participants considered learners' prior knowledge, backgrounds and experiences when selecting strategies, a practice that aligns with CLT generally (Ayres, 2006; van Merriënboer & Ayres, 2005) and the "expertise reversal effect" principle (Kalyuga <emph>et al</emph>., 2003) more specifically. Lastly, the results showed that the participants used other instructional strategies (eg, teamwork, quizzing effect) to manage CL beyond those provided in the <emph>CL Principle Guide</emph>. For example, Erin noted that she used teamwork to reduce CL. Although collaborative activities is not listed as one of the CLT strategies discussed in the literature, previous research has shown that when there is a complex task, meaningfully designed collaborative activities help to manage CL as "complex learning tasks could be shared among group members" (Kirschner, Paas, & Kirschner, 2009, p. 306). Further, collective working memory has been considered as another CL effect specifically in computer‐supported collaborative environments (see Janssen & Kirschner, 2020; Sweller <emph>et al</emph>., 2019).</p> <p>One note of interest is the referral by our participants to K12 strategies (ie, scaffolding, chinking and differentiation) when discussing CLT strategies, which led our team to look deeper into these phenomena by comparing them to the CLT principles. We found that they corresponded to specific CLT principles, indicating our instructional designers were able to make sense of the principles as we presented them for the study (<emph>CL Principle Guide</emph>, Appendix A) based on their K12 experience and training. For example, there is a parallel between differentiation and the "expertise reversal principle," a concept involving fading and prior knowledge (Kalyuga <emph>et al</emph>., 2003). Similarly, there is broad overlap between the segmenting principle and chunking (Fry, 2018; Mayer & Pilegard, 2014). Finally, we see scaffolding again when we consider the (simple to complex) sequencing principle (van Merriënboer <emph>et al</emph>., 2003) and the worked examples principle (Renkl, 2014). The K12 realm is not customarily associated with CLT and there has been limited research conducted in that realm (see Ayres, 2006; Leahy & Sweller, 2011; Mousavi, Low, & Sweller, 1995; Olina, Reiser, Huang, Lim, & Park, 2006; Paas, 1992; Scharfenberg & Bogner, 2010; van Merriënboer, Kester, & Paas, 2006), including the training of K12 teachers in CLT (see Broyles, Epler, & Waknine, 2011).</p> <hd id="AN0148800735-35">Proposition 3. Cognitive load and online course quality</hd> <p>Our results showed that while the majority of the participants applied CLT strategies intuitively, they all discussed managing CL as an element of online course quality. Several specifically mentioned that CLT strategies alone were not enough to ensure the quality of an online course, corresponding with Sentz <emph>et al</emph>. (2019). This disconnect could be related to the lack of "exposure to the application of theory" in instructional design programs (Sentz <emph>et al</emph>., 2019, p. 216). The implication from this is straightforward; if research has shown that CLT can be a factor in students' learning effectiveness and satisfaction with learning, it should be integral in instructional design programs.</p> <hd id="AN0148800735-36">Implications and conclusion</hd> <p>Several of our participants mentioned that CLT and the principles were not something that they thought about directly prior to this study. As Ann explained, "there are just so many theories" and "you know... it's very difficult because, and I realized this as I was rereading this survey, there are a lot of conflicting theories and conflicting paths to teaching that don't work for everyone." Given this and the outcomes of this study, additional research should be conducted into how instructional designers can best be trained in CLT and related theories. Furthermore, our participants often referred to K12 strategies when discussing their use of CLT strategies in their practice. This raised the question "What connections are our instructional designers, and former teachers, making to CLT strategies through parallel strategies?" The answer to this question would provide us with insights into K12 teachers' actual implementation of CLT strategies in their context ranging from the reason(s) for which each strategy is run and how those reasons are informed by CLT. Moreover, the findings showed that even though some instructional designers use CLT strategies intuitively, they all agreed that it contributes to online course quality as well as student learning. Future research could also focus on how instructional designers integrate these strategies into the systematic instructional design process to enhance online course quality. At the same time, more research is needed to evaluate the relationships between inputs (eg, CLT strategies) and outputs (eg, course quality, student satisfaction) in fully online courses in higher education, especially when it comes to evaluating online courses (Esfijani, 2018). Finally, another area of importance stressed by our participants is taking into account learner differences when designing. How can/should instructional designers differentiate their CLT strategies based on learners' level, disciplinary area, cultural differences, etc?</p> <p>While these findings make a unique contribution to the research considering instructional designers' perspectives on online course quality and how they perceive the link between online course quality and CL, there are nonetheless limitations. First, data were gathered from instructional designers from the same institution. Future research could consider using participants from other institutions to consider the institutional impact on this topic. Also, case study research is typically not considered generalizable to new situations and contexts, as such this study has attempted to provide rich descriptions to assist researchers who are interested in implementing similar research in their own contexts. Future research related to instructional design decision making would benefit from further details, including think‐aloud and/or journaling efforts.</p> <p>In conclusion, this multiple case study provided a deep understanding of instructional designers perspectives on online course quality, application of CLT strategies while designing online courses and how application of these strategies contribute to the online course quality. The results showed that instructional designers defined online course quality based on established rubrics and their personal experiences. Additionally, instructional designers considered managing CL as an element of online course quality and could identify various CLT strategies from their own work. Given instructional designers' intuitive use of CLT strategies and the disconnect between practice and perspective, the results suggest incorporating CL in instructional designer training programs. From a practical perspective, the <emph>CL Principle Guide</emph> (Appendix A) created for this study may serve as a useful tool for instructional designers.</p> <hd id="AN0148800735-37">Statements of open data, ethics and conflict of interest</hd> <p>The current research data are available from the authors upon request.</p> <p>The research received approval from the institutional ethics committee approval. We used pseudonyms for the anonymity and confidentiality of the participants.</p> <p>We do not have any conflicts of interest to declare in relation to this work.</p> <p>A Appendix Cognitive load principle guide, including definitions, codebook notes and participant examples</p> <p></p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Code(s)</th><th align="left">Definition</th><th align="left">Participants examples</th></tr></thead><tbody><tr><td align="left">CLT Strategies (Definitions from Principle Guide provided to Participants)</td></tr><tr><td align="left">Cueing (Strat_Cue)</td><td align="left">Refers to the idea that multimedia learning materials become more effective when cues are added that guide learners' attention to the relevant elements of the material or highlight the organization of the material (Hattie & Yates, <xref ref-type="bibr" rid="bibr11">2014</xref>)</td><td align="left">"when I make like a Camtasia video that's a screen capture video, have a fly‐in, pop‐in, call‐outs, right? That repeat what you're saying, but in a nice clear way. Have arrows come in, right? Have circles drawn"</td></tr><tr><td align="left">Feedback (Strat_Feedback)</td><td align="left">Refers to the idea that novice students learn better with explanatory feedback than with corrective feedback alone. <italic>Explanatory feedback</italic> provides the learner with a principle‐based explanation of why his or her answer was correct or incorrect, whereas <italic>corrective feedback</italic> merely informs the learner that his or her response was correct or incorrect (Moreno & Mayer, <xref ref-type="bibr" rid="bibr25">2007</xref>)</td><td align="left">"... the feedback is the hints and the quizzes that I do to scaffold the learners"</td></tr><tr><td align="left">Goal‐free (Strat_Goal‐free)</td><td align="left">Instead of conventional tasks that provide a problem requiring a specific solution, goal‐free tasks provide a problem with a nonspecific goal with a larger number of possible solutions (van Merriënboer & Sweller, <xref ref-type="bibr" rid="bibr49">2010</xref>)</td><td align="left">"So in terms of cognitive load that could be a nightmare but in terms of... especially for gifted education, providing those and the higher level education, providing those tasks that don't have definitive answers, is a much higher goal I think if we really want to understand"</td></tr><tr><td align="left">Modality (Strat_Modal)</td><td align="left">Replace a written explanatory text and another source of visual information (unimodal) with a spoken explanatory text and the visual source of information (multimodal) (Mayer & Pilegard, <xref ref-type="bibr" rid="bibr24">2014</xref>)</td><td align="left">"So he is talking through the lecture, and he's got his PowerPoint slide"</td></tr><tr><td align="left">Multimedia (Strat_Multi)</td><td align="left">Use of multiple media (pictures, video, audio, text) to convey a concept; the idea that people can learn more deeply from words and pictures than from words alone (Mayer, <xref ref-type="bibr" rid="bibr23">2014</xref>, p. 1)</td><td align="left">"What you can see it's kind of a complex equation... a nice color version of that, and it's kind of a job aid for the students so they can print this out and look at it to kind of remember what he was talking about"</td></tr><tr><td align="left">Pre‐training (Strat_Pre‐train)</td><td align="left">Moreno and Mayer (<xref ref-type="bibr" rid="bibr25">2007</xref>) explained pre‐training principle as "students learn better when they receive focused pretraining that provides or activates relevant prior knowledge" (p. 316)</td><td align="left">"There's a lot of vocabulary, people's names, family trees, all those things to try to wrap your brain around, keeping everybody straight, the cast of characters across Greek myth especially, there is a section for knowledge, and deeper understanding, where the students are given alternative text, and self quizzes, where they can go through and test their vocabulary, make sure they're keeping the people straight, but none of that is graded per se. It leads up towards the major assessments, and so it gives them the opportunity to master in whatever way works best for them."</td></tr><tr><td align="left">Redundancy (Strat_Redund)</td><td align="left">Redundancy occurs when the same information is unnecessarily presented concurrently in multiple forms. To avoid, replace multiple sources of information that are self‐contained (ie, they can be understood on their own) with one source of information (Kalyuga & Sweller, <xref ref-type="bibr" rid="bibr17">2014</xref>)</td><td align="left">"Avoiding redundancy is important for helping to avoid clutter so that students can focus. But I also like to provide alternative methods for learning... in an unobtrusive way"</td></tr><tr><td align="left">Segmenting (Strat_Segment)</td><td align="left">Allowing learners to work through a complex lesson in shorter segments rather than as one continuous lesson. This can be accomplished by breaking the lesson into manageable, learner‐controlled segments (van Merriënboer <italic>et al</italic>., <xref ref-type="bibr" rid="bibr48">2003</xref>)</td><td align="left">"My students said that the videos were easy to follow... were clear, and they liked being able to jump to sections... Segmenting of the pieces, the complex pieces into shorter sections"</td></tr><tr><td align="left">Self‐explanation (Strat_Self‐explain)</td><td align="left">Allows learners to provide explanations for the weak and strong aspects of their performance, and come up with strategies for improvement (van Merriënboer & Sweller, <xref ref-type="bibr" rid="bibr49">2010</xref>)</td><td align="left">"I actually have a section after they try to solve the difficult problem, to give each other tips and tricks, so to be able to reflect on..."</td></tr><tr><td align="left">Sequencing (Strat_Sequence)</td><td align="left">Learners begin with relatively simple learning tasks and progress toward more complex tasks (van Merriënboer <italic>et al</italic>., <xref ref-type="bibr" rid="bibr48">2003</xref>)</td><td align="left">"It's kind of like small [pieces] and then building up to bigger pieces... the goal is to reinforce the application... of concepts to a decision. In the application, we use a case study and require the students to develop, a framework, summarize data, analyze the problem, and utilize different tools, and draw conclusions."</td></tr><tr><td align="left">Split‐attention (Strat_Split)</td><td align="left">Split attention occurs when the essential information is divided (in time or place) between two or more sources, forcing learners to mentally integrate these different sources into one (van Merriënboer & Sweller, <xref ref-type="bibr" rid="bibr49">2010</xref>)</td><td align="left">"So, for example, if this telephone keypad cipher. If I didn't have this picture of this telephone here and I said, 'Okay, so go ahead and create this cipher using a telephone keypad.' If you weren't looking at the telephone, you're not going to be able to do that. And if you have to go pick up whatever you're doing to go look at a telephone, now, I've disrupted that learning environment because I have separated you from the task. And so I risk losing people that way."</td></tr><tr><td align="left">Variability (Strat_Variab)</td><td align="left">Replace a series of tasks with similar surface features with a series of tasks that differ from one another on all dimensions on which tasks differ in the real world (van Merriënboer & Sweller, <xref ref-type="bibr" rid="bibr49">2010</xref>)</td><td align="left">"Every time we have a module, we also have a video of a guest, and this is somebody in industry... And so we're interviewing him, and he's talking about the concept as well, like how it's utilized in business. And so they were seeing the same thing within different media and being explained different ways."</td></tr><tr><td align="left">Worked‐example (Strat_Worked)</td><td align="left">Examples that consist of a problem formulation and the final solution. They typically also include solution steps leading to the final solution; this is especially true if the worked examples demonstrate algorithmic solution procedures (van Merriënboer & Sweller, <xref ref-type="bibr" rid="bibr49">2010</xref>)</td><td align="left">"He's got Camtasia open, and he's doing some calculations in a spreadsheet, which is what they are gonna do for the assignment. But he's kind of doing, like, a practice assignment for them all the way through."</td></tr><tr><td align="left">CL Other Strategies (Strat_Other)</td><td align="left">Additional strategies mentioned by participants directly related to cognitive load (eg, not quality)</td><td align="left" /></tr><tr><td align="left">Quality in Online Courses</td></tr><tr><td align="left">Definition of quality online course (QUAL_Def)</td><td align="left">Definitions or descriptions provided given by a participant on what a quality course is or what it looks like</td><td align="left">" It's really more simply about design, and making sure that, from the students' perspective, expectations are clear, that there is alignment between outcomes and assessments, which you could argue that's pedagogical. But in the terms of how people learn, and what kind of activities you can engage them in to really foster those learning experiences."</td></tr><tr><td align="left">Example(s) of quality online courses (QUAL_Examp)</td><td align="left">Verbal or visual examples provided by the participant illustrating principles of quality course design</td><td align="left">"... this is just laid out.... It's by date. And I organized this material very similarly by let's spread it out, let's not have a bunch of due dates, you know, on top of each other. And we've got different activities. We're at Capsim, is a simulation game, so they're doing that, and that's a team activity. In March, they're doing some, you know, you've heard a 360‐degree feedback that they had to do that. And they're sharing their results."</td></tr><tr><td align="left">Instructional strategies (QUAL_InstrStrat)</td><td align="left">Strategies implemented in the course that assist in conveying information to students (eg, interaction, feedback, motivation, technology integration, variety) Hint: If it's something found in the implementation phase of ADDIE, it's instruction</td><td align="left">"felt like that really improved quality. Quizzing as a form of instruction. So the cognitive... Oh, what do they call it? The quizzing effect. I think that actually does improve the quality of a course. It certainly improved the quality of my..."</td></tr><tr><td align="left">Design strategies (QUAL_DesignStrat)</td><td align="left">Elements of the design of the course itself that improves the quality of the course (eg, navigation, alignment, sequencing, chunking) Hint: if it's something found prior to the implementation phase of ADDIE, it's design.</td><td align="left">"as much as you can chunk information and...but also think about how it's... how one thing leads to the next, to be able to see the connections because I think that's one area where if you start chunking information, you might lose that connection if you can't find a way to put it together well."</td></tr><tr><td align="left">Cognitive load</td></tr><tr><td align="left">Understanding of CL (CL_Understand)</td><td align="left">Definition of CL or insight given by participant about their understanding (accurate or not) about cognitive load theory or its associated principles; also principles relating to their lack of understanding of cognitive load (see also misconceptions)</td><td align="left">"how much brain power you need to focus in and learn the materials."</td></tr><tr><td align="left">CL Experience (eg, Background) (CL_Exper)</td><td align="left">Educational or work experience of instructional designers leading to an understanding of cognitive load</td><td align="left">"So I was always trained that you start off with simple to complex, that you give the opportunity to do micro learning. So small tasks, once you've mastered that you loop back, you repeat, and you keep revisiting the same concept multiple times, each time a little more difficult so that it's not overwhelming to learners. And they have the opportunity to become comfortable with terminology, processes, and their understanding as they become more adept to meeting the goals for the course, or the concept."</td></tr><tr><td align="left">Intentionality of use of CL in design work (CL_Intent)</td><td align="left">Intentional or intuitive use of CL strategies in the design process</td><td align="left">"So it built up over time. I think that I probably came at it from the instructional design to the theory, so working on what worked. You know, and some of it was just as an instructor doing the wrong thing and learning from it."</td></tr><tr><td align="left">Examples of Cognitive Overload (CL_OverloadExamp)</td><td align="left">Verbal or visual examples provided by the participant demonstrating cognitive overload (eg, videos or readings are too long)</td><td align="left">"Also, cognitive overload, I think would occur if you just showed the system and you weren't actually kind of talking the students through how to look at it. Or if you were introducing them to say a table and you didn't explain how to look at the table, I think that's cognitive overload, because now, you just showed me this whole table. I don't even know where to start looking."</td></tr><tr><td align="left">Cognitive load and quality in online courses</td></tr><tr><td align="left">Quality/CL relationship (QUCL_Relation)</td><td align="left">Insight given by participant on the relationship between course quality and cognitive load</td><td align="left">"I think even though there are many, many different rubrics out there of what course quality is and many different ways to approach cognitive load, the foundational pieces are the same across the board between those instruments and processes. Everything is about clarity, and understanding, and simplification with appropriate redundancy....... I think if we keep that in mind, both in design and delivery, that we'll achieve more of what we aim to with student understanding and faculty satisfaction with the outcomes of the courses."</td></tr><tr><td align="left">Strategy recommendations (QUCL_StratRec)</td><td align="left">Recommendations by participants on designing to balance cognitive load in learners</td><td align="left">"I think that that marries that quality and cognitive load, it takes a lot of time to parse out that fluff and get to really the nuggets that you really want to present and the core of that information, but the more you can do that, the higher the quality of the course is going to be because it allows you to isolate information so that the students can look at it in those small pieces."</td></tr></tbody></table> </ephtml> </p> <p>B Appendix Interview protocol</p> <p>Thank you for agreeing to participate in the interview and for completing the pre‐survey in advance. We appreciate you taking the time out of your busy schedule! Also, we do need to collect a signed consent form from you if we have not already.</p> <p>As a reminder, the purpose of our study is to determine: How do instructional designers perceive the relationship between the level of quality in online course design and cognitive load strategies?</p> <p>Just to let you know, participation is voluntary and you are free to withdraw at any time. Have you had a chance to review the project consent form? Do you have any questions related to this?</p> <p>Do I have your permission to record this session (once permission is granted then turn on recorder and verify so they are on "tape" granting permission).</p> <p> <emph>Please feel free to refer to the Cognitive Load Principle Guide and/or your survey responses (copy provided) as needed throughout the interview</emph>.</p> <p>Background</p> <p>In your pre‐survey, you have listed that you work as an instructional designer at __________. Could you please describe in more detail your role and responsibilities?</p> <p>Quality</p> <p></p> <ulist> <item> How do you know you've designed a quality online course?</item> <p></p> <item> ○ I see in your pre‐survey you indicated you use ____ and ____ as elements for a quality online course. How do you see these fitting together?</item> <p></p> <item> When you are designing a course, what design or instructional strategies do you include to ensure quality?</item> <p></p> <item> ○ Can you share an example of a course you consider to be a high quality course and talk about the strategies you just mentioned? (eg, share screenshots, provide access to course/master, provide print outs)</item> </ulist> <p>Cognitive load</p> <p></p> <ulist> <item> Can you describe for us your current understanding of cognitive load? This is in no way an evaluation of your knowledge as we believe most instructional designers intuitively plan for cognitive load, if not purposefully.</item> <p></p> <item> ○ <emph>[If understanding is provided]</emph> Can you tell us a bit about your background and experiences in this area?</item> <p></p> <item> ○ [ <emph>Note: refer to survey responses provided to interviewee]</emph></item> <p></p> <item> In the survey, you indicated you use (a number, several,...) of cognitive load strategies. Would you say these are conscious choices based on what you know about cognitive load or more intuitive based on what you know about instructional design or developing a quality course?</item> </ulist> <p>Show‐and‐tell cognitive load strategies</p> <p></p> <ulist> <item> If possible, could you describe or show us an example of a course design or activity that you feel represents cognitive overload? It may be something you have come across outside of your work or something you noticed prior to your work on a course.</item> <p></p> <item> ○ What factors were you concerned about in this activity? Why?</item> <p></p> <item> You mentioned in the pre‐survey you use ______ principles associated with cognitive load.</item> <p></p> <item> ○ Could you show an example(s) where cognitive load strategies were incorporated? (eg, share screenshots, provide access to course/master, provide print outs)</item> <p></p> <item> ○ What factors were you concerned about when designing this activity? Why?</item> <p></p> <item> ○ Can you tell us more about how and why you use these strategies?</item> <p></p> <item> When you were designing this/these course(s)/activity(ies) did you intentionally include these strategies to manage cognitive load or were you primarily focused on other instructional design/course quality "best practices"?</item> <p></p> <item> How would your design strategies, specifically those related to cognitive load, change if you were designing an intro course versus and an advanced course?</item> <p></p> <item> Do you vary strategies based on discipline (hard sciences, humanities)?</item> </ulist> <p>Relationship between quality + CL</p> <p></p> <ulist> <item> Thinking about your overall experiences as an instructional designer, how would you describe the relationship between online course quality and cognitive load?</item> <p></p> <item> ○ <emph>[If relevant, as in specific activities cited]</emph> Can you share a few examples with us?</item> </ulist> <p>Recommendations</p> <p></p> <ulist> <item> What strategies would you recommend to instructional designers that would help balance the cognitive load for learners and promote quality in online courses?</item> </ulist> <p>Closing out</p> <p></p> <ulist> <item> Is there anything else you would like to share that may be related to course quality or cognitive load that we have not discussed?</item> </ulist> <p> <emph>On behalf of our research team, thank you again for your time in participating in this interview. Please feel free to reach out to us by email or phone if you have any questions or concerns. We will contact you for verification as we conduct the analysis</emph>.</p> <ref id="AN0148800735-38"> <title> References </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Almutairi, A. F., Gardner, G. E., & McCarthy, A. (2014). Practical guidance for the use of a pattern‐matching technique in case‐study research: A case presentation. Nursing and Health Sciences, 16 (2), 239 – 244. https://doi.org/10.1111/nhs.12096</bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">2</bibl> <bibtext> Ayres, P. (2006). Impact of reducing intrinsic cognitive load on learning in a mathematical domain. Applied Cognitive Psychology, 20, 287 – 298. https://doi.org/10.1002/acp.1245</bibtext> </blist> <blist> <bibl id="bib3" idref="ref3" type="bt">3</bibl> <bibtext> Baldwin, S., Ching, Y.‐H., & Hsu, Y.‐C. (2018). Online course design in higher education: A review of national and statewide evaluation instruments. 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Thousand Oaks, CA : Sage.</bibtext> </blist> </ref> <aug> <p>By Secil Caskurlu; Jennifer C. Richardson; Hamdan A. Alamri; Katherine Chartier; Tadd Farmer; Shamila Janakiraman; Marquetta Strait and Mohan Yang</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author; Author</p> <p></p> <p>Secil Caskurlu is a Postdoctoral Research Associate in the Department of Counseling, Educational Psychology and Special Education at Michigan State University. Secil's research focuses on designing and developing meaningful learning experiences to promote student learning process and outcomes, the Community of Inquiry (CoI) framework, competency‐based education, computational thinking integration in K12.</p> <p>Jennifer C. Richardson is a Professor in the Learning Design and Technology program at Purdue University. Jennifer's research focuses on evidence‐based practices in online learning environments and how the CoI framework can be used for improving instructor practices. Jennifer has been teaching and conducting research for the past 20 years and is a Fellow of the Online Learning Consortium. Jennifer has received awards related to leadership and research in her field, including the Online Learning Journal Outstanding Research Award in Online Education (2017, Online Learning Consortium), the AERA SIG Instructional Technology Leadership Award and the Sloan‐C Effective Practices in Online Education Award.</p> <p>Hamdan A. Alamri is an Assistant Professor in Learning Design and Technology at King Saud University. His research interest focuses on the systemic change of education toward learner‐centered environments. In practice, Hamdan designs personalized learning environments and integrates learner‐centered technologies to motivate and engage learners in both, K‐12 and higher education.</p> <p>Katherine Chartier is a doctoral candidate in the Department of Curriculum and Instruction, Learning Design and Technology program at Purdue University.</p> <p>Tadd Farmer is a doctoral student in the Department of Curriculum and Instruction, Learning Design and Technology program at Purdue University. Tadd's research focus is on researching and designing effective online learning experiences through the application of self‐efficacy and self‐determination theories.</p> <p>Shamila Janakiraman is a doctoral candidate in the Department of Curriculum and Instruction, Learning Design and Technology program at Purdue University. Shamila's research interests are in game‐based learning, attitude change instruction, online teaching and learning, MOOCs and adult education. Specifically, Shamila's research focuses on the use of game‐based learning to change attitudes in environmental sustainability education and other socio‐ scientific topics.</p> <p>Marquetta Strait is a doctoral student in the Department of Curriculum and Instruction, Learning Design and Technology program at Purdue University. Marquetta is interested in examining the intersection of instructional design and peer learning in collegiate environments that are designed to cultivate deeper level knowledge of course content.</p> <p>Mohan Yang is a doctoral student in the Department of Curriculum and Instruction, Learning Design and Technology program at Purdue University. Mohan's research interests are in online experiential learning, authentic learning, transfer of training and instructional design.</p> </aug>
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  Data: Cognitive Load and Online Course Quality: Insights from Instructional Designers in a Higher Education Context
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  Data: <searchLink fieldCode="AR" term="%22Caskurlu%2C+Secil%22">Caskurlu, Secil</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8716-9586">0000-0001-8716-9586</externalLink>)<br /><searchLink fieldCode="AR" term="%22Richardson%2C+Jennifer+C%2E%22">Richardson, Jennifer C.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7534-1406">0000-0001-7534-1406</externalLink>)<br /><searchLink fieldCode="AR" term="%22Alamri%2C+Hamdan+A%2E%22">Alamri, Hamdan A.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8350-4383">0000-0001-8350-4383</externalLink>)<br /><searchLink fieldCode="AR" term="%22Chartier%2C+Katherine%22">Chartier, Katherine</searchLink><br /><searchLink fieldCode="AR" term="%22Farmer%2C+Tadd%22">Farmer, Tadd</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2549-1232">0000-0003-2549-1232</externalLink>)<br /><searchLink fieldCode="AR" term="%22Janakiraman%2C+Shamila%22">Janakiraman, Shamila</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8750-3601">0000-0001-8750-3601</externalLink>)<br /><searchLink fieldCode="AR" term="%22Strait%2C+Marquetta%22">Strait, Marquetta</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Mohan%22">Yang, Mohan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0856-0814">0000-0003-0856-0814</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22British+Journal+of+Educational+Technology%22"><i>British Journal of Educational Technology</i></searchLink>. Mar 2021 52(2):584-605.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: <searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Quality%22">Educational Quality</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Difficulty+Level%22">Difficulty Level</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Standards%22">Standards</searchLink>
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  Data: 10.1111/bjet.13043
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  Data: This multiple case study investigates instructional designers' perceptions of online course quality, their use of cognitive load strategies when designing online courses, and whether utilization of these strategies contribute to online course quality. The participants of this study were instructional designers (n = 5) who worked in various campus programs at a large Midwestern university. Data sources included pre-interview survey, semi-structured interview and sample course design documents. Employing a pattern matching technique, the results showed that instructional designers (a) define online course quality based on established standards and rubrics; (b) apply cognitive load strategies intuitively while designing online courses; and (c) consider CLT design strategies as an element contributing to course quality. The results also showed instructional designers' use of cognitive load strategies mainly focused on reducing extraneous cognitive load. Implications for practice and research as well as directions for future research are discussed.
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  Data: EJ1286586
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      – PersonEntity:
          Name:
            NameFull: Farmer, Tadd
      – PersonEntity:
          Name:
            NameFull: Janakiraman, Shamila
      – PersonEntity:
          Name:
            NameFull: Strait, Marquetta
      – PersonEntity:
          Name:
            NameFull: Yang, Mohan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 0007-1013
          Numbering:
            – Type: volume
              Value: 52
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: British Journal of Educational Technology
              Type: main
ResultId 1