Motivational Design in Chemistry MOOCs: Applying the ARCS Model

Chapter 3

Motivational Design in Chemistry MOOCs: Applying the ARCS Model Downloaded by UNIV OF FLORIDA on October 27, 2017 | http://pubs.acs.org Publication Date (Web): October 26, 2017 | doi: 10.1021/bk-2017-1261.ch003

Kun Li* Center for Instructional Technology, Duke University, Duke University Libraries, Box 90198, Durham, North Carolina 27708, United States *E-mail: [email protected].

This chapter describes a research study in which a motivational design model – the attention, relevance, confidence, and satisfaction (ARCS) model – was applied in an effort to increase learners’ motivations in two Massive Open Online Courses (MOOCs) in chemistry. The chapter first discusses motivation in learning environments. Then, the related ideas of instructional design, motivational design, and the ARCS model are introduced. MOOCs in general, together with the issues and opportunities they potentially cause and provide, are briefly reviewed. Last, a description of an instructional design study for two chemistry MOOCs is presented with highlights on important design features in the process. The courses incorporated motivational strategies into course emails, course pages, discussion forums, and quiz feedback in the two MOOCs.Students rated attention as the ARCS component with the highest score while relevance had the lowest score. Discussions of the results are provided.

Motivation Motivation often refers to “why people think and behave as they do” (1). Graham and Weiner further categorized motivation into “the choice of behavior”, “the latency of behavior”, “the intensity of behavior”, “the persistence of behavior”, and “the cognitions and emotional reactions accompanying the behavior” (1). People with motivations toward certain things will be active in © 2017 American Chemical Society Sörensen and Canelas; Online Approaches to Chemical Education ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

doing these things, while those who are not motivated will behave passively in performing similar tasks (2). Different people have motivations toward different aspects of their lives. Even for the same person toward the same thing, motivation is not constant in different situations or at different times (3).

Downloaded by UNIV OF FLORIDA on October 27, 2017 | http://pubs.acs.org Publication Date (Web): October 26, 2017 | doi: 10.1021/bk-2017-1261.ch003

Motivation in Learning In learning, when learners’ motivations are inspired, they show curiosity about the learning topics, immerse themselves in the learning tasks, and conduct activities that will make them learn better (4). In a blended learning environment, motivation to learn is highly positively correlated with students’ satisfaction levels with the course and course grades (5). Martens, Gulikers, and Bastiaens reported that students with higher intrinsic motivations for a particular course, who by definition are driven through internal reward mechanisms, showed more curiosity in learning and thus performed more exploratory study (6). Researchers have explored both extrinsically- and intrinsically-based strategies for increasing learners’ motivations during learning. Deci, Vallerand, Pelletier, and Ryan listed external rewards, punishment, grades in educational assessment, deadlines, competitions, and giving learners options in learning as common useful external methods to increase motivation (7). McGinley and Jones stimulated students’ intrinsic motivation mechanisms by incorporating class discussions about students’ expectations and interests in the course at the beginning of a course (8). There have been studies showing that students’ levels and types of motivations are correlated with different communication styles of teachers (9). In the digital sphere, computer games have been effectively used to promote students’ motivations in learning (10).

Instructional Design Instructional design (ID) aims to “make learning more efficient and effective and to make learning less difficult” (11). Learning theory/theories explain(s) the predictions that an instructional design theory provides, that is, why certain instructional outcomes occur under certain circumstances (12). Branch and Merrill defined ID as “a system of procedures for developing education and training curricula in a consistent and reliable fashion” (13). Dick, Carey, and Carey explained that every element of instructional design should be integrated and interacted during the design process, and they suggested that the term “instructional design” should be used as an “umbrella term that includes all the phases of the ISD (instructional system design) process” (14). Instructional design is to teaching and learning what blueprints are to buildings, for they both provide models that will later be developed or constructed in real settings (15). Compared to other theories, ID theory is prescriptive in nature rather than descriptive (15). The systematic development of ID procedures originated during World War II had been used in education for several decades prior to that (16). Multiple schools of learning theories and various newly emerged technologies have assisted the development of ID and effective use 36 Sörensen and Canelas; Online Approaches to Chemical Education ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

of instructional technology. Based on learning theories, systems theory, and philosophical perspectives, different ID models were developed. Most of the ID models include the same sequence of steps: analysis, design, development, implementation, and evaluation (13).


Motivational Design Although researchers from different fields, such as education, psychology, and communication, etc., acknowledge and attempt to increase learners’ motivation in learning from different perspectives, it is more appropriate to address motivational problems in a systematic way (17). Motivational design is “the process of arranging resources and procedures to bring about changes in people’s motivation” (18). Motivational design does not stand independently in instruction; instead, it should be incorporated into the regular ID process to better meet the instructional goals (18). In general, motivational design should start with an analysis aimed at identifying the most common motivational problem(s) exhibited by learners in a particular setting (19). There are two widely used motivational design models: the time continuum model and the attention, relevance, confidence, and satisfaction (ARCS) model. The time continuum model provides strategies to increase motivation based on three critical time points during instruction: the beginning, the middle, and the end of instruction (20), while the ARCS model, which will be introduced in the next section, focuses on four components that affect students’ motivation, not specifically targeting any instructional time period (21).

The ARCS Model The ARCS model is the most widely-used motivational design model in instructional design. It was initially conceived and created by Keller in the 1970s and 1980s (21). The model proposes that, to design motivational-enhanced instructional materials, one has to obtain and sustain learners’ attention, make the materials relevant to learners’ needs, increase learners’ confidence in learning the content, and raise learners’ satisfaction with the learning process (21). The ARCS model has proven to be effective in a range of ID projects at multiple levels of education and in various cultural contexts. As an illustration, using a designed-based research approach, Shellnut, Knowlton, and Savage applied the ARCS model into a college engineering course curriculum in the United States (22). It has also been successfully applied to K-12 settings in other countries, such as Japan (23) and Europe (24). Motivational interventions or materials designed from the ARCS model, like motivational messages, readings, emails or lectures delivered online, have been proven effective in increasing learners’ motivation in online learning settings (25). 37 Sörensen and Canelas; Online Approaches to Chemical Education ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

Massive Open Online Courses


What Are Massive Open Online Courses (MOOCs)? The ideal MOOC is “an online course aimed at unlimited participation and open access via the web” (26). Through the use of video lectures and readings made freely available to thousands of learners, MOOC proponents claim that MOOCs offer a scalable approach of disseminating information (27). Unlike open courseware, most MOOCs hosted by universities or by learning platform providers also include opportunities for interactive learner practice such as graded homework assignments and/or exams, as well as structured discussion forums, and this distinguishes them from being virtual textbooks or simple collections of videos like a YouTube channel (28). Zheng, Rosson, Shih, and Carroll differentiated MOOCs from other virtual learning environments in “scale, students’ level of control and flexibility, the relative roles of instructor and students, student motivation and outcomes” (29). Often MOOCs are offered on a dedicated MOOC provider platform. Examples of relatively large-scale MOOC providers based in the United States include Coursera (https://www.coursera.org/), edX (https://www.edx.org/), and Udacity (https://www.udacity.com/). Most MOOC providers work both with universities and corporations aiming to provide a variety of courses in a wide range of subjects of interest to learners. Ideally academic, industrial, and non-profit partners with content knowledge expertise work closely with the platform providers to design and deliver courses. There is a rapid trend towards increasing flexibility and asynchronization: more and more MOOCs offered on these platforms have been, or are being, transitioned into self-paced courses in which learners can learn at their own schedule rather than following a course over a prescribed number of weeks defined by the instructor. Engagement and Motivational Issues Extremely low course completion rate (10% and less) is the most common critique in articles or reports on MOOCs (30); however, researchers have argued that the terms “enrollment” and “student” in a MOOC are rarely defined in a way that is analogous to brick-and-mortar classrooms, so it is too restrictive to view MOOCs from just this one perspective. When visiting a course website to view a MOOC or signing up for a MOOC, different learners have different learning objectives and needs, so their levels of participation, their choices of time and effort commitment, and their learning outcomes differ as well (31). Instead of critiquing the low completion rates of enrollees in MOOCs, some researchers have explored possible reasons for what may prevent students from keeping up with their MOOCs. Since MOOCs are different from university credit-based courses, it may not be appropriate to consider the retention rate and achievement as researchers do in traditional courses (32). Some reasons for disengagement can be viewed as primarily being due to personal characteristics such as bad learning habits and low self-efficacy; whereas other reasons are more objective, like language barriers and no or limited access to the technologies required in the course (31). The flexibility of online learning means that, 38 Sörensen and Canelas; Online Approaches to Chemical Education ACS Symposium Series; American Chemical Society: Washington, DC, 2017.

likely more than students in traditional classrooms, MOOC students have to be autonomous in their own learning and to be able to manage their time well (33). Students who are not able to control their own learning, or who are not ready for this autonomous learning environment, may not be able to keep up (33).


New Opportunities Despite the concerns and critiques, MOOCs have revealed new opportunities for teaching, learning, and researching. Dellarocas and Van Alstyne pointed out that there are a variety of models that simultaneously allow MOOCs to be useful for society while also allowing MOOC providers to make a profit (34). For example, MOOC providers could provide students’ learning analytics to prove competencies to employers, or invite large corporations as sponsors for mutual benefits, to name a few. Researchers have predicted that the trend of campuses creating MOOCs will ultimately impact university’s residential students’ learning (32). For example, researchers in education, computer science, and statistics now collaborate closely to examine the massive data generated by the large numbers of students in MOOCs by building models and algorithms to understand students’ behaviors, the relationships between behaviors, as well as achievement, persistence and so on (35). Knox adopted constructivism and connectivism to provide pedagogy in his MOOC that was different from other MOOCs based on behaviorism (36). In response to the critique of the lack of interaction in MOOCs due to large numbers of students, researchers have attempted to prove the pedagogical value of discussion forums and peer-assessed assignments in MOOCs. With massive data generated from two MOOCs, it was found that, although few students participated in the MOOC discussion forums, discussion was shown to enhance learning and understanding for those students who did participate (37). Some MOOC providers have incorporated peer assessments as a part of their platform features to increase peer support and interaction (38). Although Kulkarni et al. reported a significantly correlated result of peer grades with staff grades, computer scientists and statisticians continue working to create models that can increase peer assessments’ grading accuracy (39). Learners’ Motivations in MOOCs There are several studies examining MOOC students’ motivations for signing up for specific MOOCs. It has been pointed out that it is the learners’ own goals that determine how they learn, not the course itself (40). When learners were allowed to choose only one of many options for indicating their initial motivation for taking edX’s Circuits and Electronics course, over half of the respondents reported that they took the course to gain knowledge and skills (41). Duke University allowed students to choose several options when indicating their initial motivations for enrolling in Duke’s first MOOC on Coursera: Bioelectricity. The most often selected motivation was “general interest in the topic”, followed by “extending current knowledge of the topic” (42). Breslow et al. did not find a relationship between students’ motivations for enrolling in edX’s Circuits and 39 Sörensen and Canelas; Online Approaches to Chemical Education ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Electronics course and their learning achievement, which was measured by grades earned, in the course (32). Studies have found two major reasons for learners to join MOOCs: for interest/curiosity and for professional needs (43). Besides these two major motivations, learners enroll to improve their English skills, to interact with peers, or to learn a hobby, etc. (44) E-learning in general is considered to impose challenges on students’ motivation (45), and MOOCs constitute a special case in e-learning because they can bring new and more challenging motivational problems. Low completion rates in MOOCs shows the motivational problem from one perspective. Feelings of isolation and few or no interactions with the instructor in MOOCs can decrease learners’ motivation. There is little published research to date that examines the intricacies of sustained motivational issues existing in MOOCs. There is even less research on motivational design that is intended to increase learners’ motivation while taking MOOCs. The next section highlights a study that applied the ARCS model systematically in two chemistry MOOCs to increase learners’ motivation.

Highlights from Designing Motivational Strategies in Chemistry MOOCs Based on the previous discussion on the relatively low completion rates in MOOCs, it can be assumed that learners have initial motivations when enrolling in a specific MOOC, but that these initial motivations are not sustained long enough for the learner to get through the entire course. As instructional designers, we may wonder how we can use ID theories and models to help learners maintain or increase their motivations when taking MOOCs. This section briefly describes a research study in which the ARCS motivational design model was applied to motivate learners in two chemistry MOOCs. The study used Keller’s ten-step method when designing ARCS motivational strategies (18). The ten steps are: “obtain course information, obtain audience information, analyze audience, analyze existing materials, list objectives and assessments, list potential tactics, select and design tactics, integrate with instruction, select and develop materials, and evaluate and revise” (18). Instead of explaining each step in detail, the following paragraphs focus on introducing several critical steps in this project; the detailed design process and results are described elsewhere (43). Below, the overall findings that broadly apply to ID for MOOCs are discussed. One of the most important and challenging steps in applying the ARCS model is audience analysis (18). In MOOCs, this step is even more challenging because of the broad range of audiences with diverse perspectives. For example, a traditional college classroom may have students’ ages ranging mostly from 18 to 24; a MOOC most likely has learners with ages ranging from 12 (or younger) to 80+. In addition, these learners vary in their education, career, socio-economic status, English proficiency, etc. Since audience analysis is critical to performing the following ARCS steps, a best guess approach was used to analyze audiences’ motivational profiles based on the author’s experience as a supporting staff member in the two MOOCs. The audience analysis led to a very diverse range 40 Sörensen and Canelas; Online Approaches to Chemical Education ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


in learners’ motivation profiles in terms of the ARCS components. Balancing learners with extremely high motivations (motivations that are too high are not optimal because of the potential high stress levels when encountering obstacles) and learners with much less intense or fewer motivations is an important issue to consider in the ARCS strategy design process for MOOCs. In the step of listing potential tactics for addressing this motivational problem, the author brainstormed as many ARCS strategies as possible without taking the specific learning environment – MOOC – into account. This prevented the instructional designer from being constrained by platform limitation considerations that might lead to omitting some strategies. The two chemistry MOOCs were introductory-level, session-based (not self-paced, with start and end dates) courses offered on Coursera. The author had been working as a supporting staff member for the two MOOCs. The two MOOCs included video lectures and practice exercises that were provided each week, optional bi-weekly advanced problem sets, and a mid-term and a final exam. Motivational strategies needed to be incorporated into all or some of these course components. After careful analyses and diagnostics on the course components, the video lectures were considered the most well received by the previous students. The analysis of the videos in terms of the ARCS components was completed, and this revealed that the video lectures already included many ARCS strategies. In the course revision, it would be difficult to add even more ARCS strategies to video lectures – it would involve substantial time and effort from the instructor, the author, and video facilities and support staff. Considering the project timeline, the motivational strategies were instead integrated into course announcements/emails, pages, exercise feedback, and discussion forums. The motivational strategies that were selected to final design into the MOOCs included: (1) in the course registration welcome email, provided links and screen captures of the course to increase attention and relevance. (2) Provided grading formula and flexibility on deadlines for graded quizzes to increase attention and confidence. (3) Included a discussion forum self-introduction post to increase attention. (4) Provided previous students’ statement about their perceptions of the course and how they applied the knowledge to enhance relevance and confidence. (5) Provided feedback and encouragement in the quiz feedback to increase confidence. (6) In the course weekly emails, summarized the week’s topics and answered common questions to increase attention and relevance. (7) After the course was over, summarized the entire course and provided future learning topics to increase attention, relevance, and satisfaction. (8) In the email after certificate was relased, congratulated students on their achievement to increase satisfaction. After selecting and designing the ARCS strategies, they needed to be integrated into the instruction carefully and seamlessly. The ARCS motivational design should be accomplished with instructional design, not separate from it. In order to do this, the author listed motivational objectives together with content-knowledge objectives and developed weekly lesson plans to integrate them into instruction. Long-term plans to evaluate the results from launching new motivational strategies should also be designed before implementing these strategies. Due to platform constraints (multi-group experimental design on Coursera was not available at that time), the evaluation methods in this study 41 Sörensen and Canelas; Online Approaches to Chemical Education ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


included a questionnaire Instructional Materials Motivation Survey (IMMS), which was designed specifically for the ARCS model, interviews with learners, and a design journal that the author kept to record important decisions in the design process (43). This study adopted an exploratory approach to evaluate the motivational strategies that were integrated into the MOOCs. The results of the study showed learners’ overall positive attitudes toward the implemented ARCS motivational strategies and the courses in general. In both courses, learners reported that attention was the component with the highest score while relevance had the lowest score. The high attention score indicates that participants seemed to have no problems focusing on information that they seeked. The low relevance score could be due to the different motivations and goals that learners had for MOOCs; perhaps the relevance strategies that were designed did not always align with the learners’ own definitions of relevance? For example, course emails summarizing important concepts covered in the course in that week might be relevant to learners who seeked the knowledge and skills, but irrelevant to learners who would like to improve their English. Future studies could design the ARCS motivational strategies into MOOCs and measure their effectiveness by implementing an experimental design with a control group. Learners’ motivations and behaviors differ in MOOCs with radically different topics, such as MOOCs that are designed for self-fulfilling purposes as opposed to MOOCs designed primarily for career advancement. It would be interesting to investigate how learners react to the ARCS motivational strategies in these different types of courses and determine which ID strategies are most effective in the different MOOCs.

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Motivational Design in Chemistry MOOCs: Applying the ARCS Model

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