The Impact of Lecture Webcasts and Student Self-Regulated Learning on Academic Outcomes
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The growth of online technologies and their incorporation into learning environments is based on the expectation that including technologically-based supportive tools into a blended learning environment will substantially improve students\’ learning outcomes. However, very little is known about the motivational, cognitive, and behavioural self-regulation attributes that may contribute to student success in blended learning. Using a social cognitive view of selfregulated learning as a theoretical framework (Pintrich, 1999, 2004; Zimmerman, 1989,1998,& 2002) the present study examined the relations between students\’ self-regulation attributes and their academic outcomes in a blended learning course that provided the webcast recording of the face-to-face lectures, online access to weekly quizzes and course assignment and question/discussion boards. Additionally, this study examined whether webcast viewing was associated with students` academic outcomes in the course. A small, but significant positive correlation was found between students\’ overall viewing times and their academic outcomes in the course. Students were generally more likely to view the webcasts either immediately after the weekly lecture or on the days immediately preceding their scheduled exam in the course. An exploratory path analysis indicated that intrinsic goal orientation, time and environment management, effort regulation, and self-efficacy had significant impacts on students’ final grades. Students with low self-regulation skills could benefit from webcasts as long as they were driven by intrinsic rewards and could direct their efforts to the task at hand.
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The Impact of Lecture Webcasts and Student Self-Regulated Learning on Academic Outcomes
- 1. The Impact of Lecture Webcasts and Student Self-Regulated Learning On Academic Outcomes Nima Hejazifar, M.Sc. Applied Modelling and Quantitative Methods Trent University
- 2. This talk presents an exploratory model of self- regulation in a blended learning environment 1. Blended Learning 2. Self-Regulated Learning Performance Face to Phase Face Blended Learning + Self- Forethought Reflection Phase Phase Online 4. The Exploratory Model of 3. Evaluation of Blended Learning Self-Regulation and Webcasting at Trent University Motivational Factors Cognitive Final Webcast Factors Grades Viewing Behavioural Factors
- 3. Blended learning is the combination of online and face-to-face learning traditional or web facilitated (1 to 29% of the contents Face to online) face learning Blended + learning 30 to 79% of the contents online Online learning 80+% of the contents online
- 4. It is very important for instructors to have a clear objective when introducing blended learning to students Face to Categories of Blended Learning face learning Enabling blends Enhancing blends Blended + learning Transforming blends Online learning
- 5. Blended learning provides the best of both worlds Face to Advantages face Control the pacing and location learning of learning Blended Flexibility to Review material learning Online learning Disadvantage Procrastination
- 6. Using the social cognitive view of self-Regulated learning to examine academic performance in a blended setting Performance Phase Self-Control Self-Observation Forethought Phase Self-reflection Phase Task analysis Self-judgment Self-motivation Self-reaction beliefs
- 7. Forethought Phase refers to processes that take place before efforts to learn Performance Forethought Phase Phase Task Analysis Goal Setting Strategic planning Forethought Self-Reflection Self-Motivation Beliefs Phase Phase Self-efficacy Outcome expectation Intrinsic interest/value Learning goal orientation
- 8. Performance phase refers to the processes that take place during the application of behaviour Performance Performance Phase Phase Self-Control Imagery Self-instruction Forethought Self-Reflection Attention focusing Phase Phase Task strategies Self-Observation Self-recording Self-experimentation
- 9. Self-reflection phase refers to processes that take place after each learning effort Performance Self-Reflection Phase Phase Self-judgment Self-evaluation Causal attribution Forethought Self-Reflection Self-Reaction Phase Phase Self-satisfaction/affect Adaptive/defensive
- 10. To date, four specific self-regulatory dimensions are known to play a role in blended settings Intrinsic goal orientation Self-efficacy Time and environment management Help seeking
- 11. Webcast was selected as the primary online tool for the introduction to psychology blended course at Trent University
- 12. Methodology Participants 451 students (340 female and 111 male) Measures Motivated Strategies for Learning Questionnaire (MSLQ) Participants’ viewing time for each lecture Final grade in the course
- 13. Students viewed the webcasts either immediately after the lectures or a few days prior to the final exam
- 14. Webcast Viewing and Academic Outcome Students welcomed the addition of webcasts into the course Overall, webcast viewing was significantly and positively associated with students’ academic outcomes
- 15. This study was one of the first studies to explore the role previously unexplored self-regulatory variables in a blended learning course Task value Effort regulation Peer learning Test anxiety
- 16. Solid Lines Represent Significant Path Coefficients. Dashed lines Depict Significant Correlations When No Significant Path Coefficients Exist., *p < 0.05; **p < 0.01; ***p < 0.001. Task value Intrinsic -.06 -.07 .16* Self- efficacy .37*** Manage -.32*** Overall Final .84* .09 .33*** viewing grade Effort .28*** -.09 Help- seeking -.09 Peer -.05 learning Test anxiety
- 17. This study is an important addition to the very limited but growing field of research examining self-regulated learning in blended learning environments Students view immediately after Low SRL can benefit if they direct effort lecture or a few days before exam and are driven by intrinsic rewards Motivational Factors Cognitive Final Webcast Factors Grades Viewing Behavioural Factors Questions?
- 18. References Allen, I. E., & Seaman, J. (2010). Learning on demand: Online education in the United States, 2009. Needham, MA: Sloan Center for Online Education. Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. Bonk & C. Graham (Eds.), The Handbook of Blended Learning: Global perspectives, local designs. San Francisco, CA: Pfeiffer Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385-407. Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. L. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53, 801-813. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41, 64-70.
- 19. Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45, 166-183.
Notes de l'éditeur
- Good afternoon, I like to give you a very sincere welcome to today’s presentation on the impact of webcasts and student self-regulated learning on academic outcomes. My name is NimaHejazifar and I am a master students in the applied modelling and quantitative methods at trent university. We live in a very interesting time. It is a time where technology has become an integral part of the educational system. Traditionally, the growth of technology is based on the expectation that technologically based supportive tools wouldsubstantially improve students’ learning outcomes. However, very little is known about the motivational, cognitive, and behavioural attributes that may contribute to academic success in technologically enhanced classrooms.
- Using self-regulated learning as a theoretical framework (Pintrich, 1999, 2004; Zimmerman, 1989, 1998, & 2002) this presentation will examine the relations between students' self-regulatory attributes and their academic outcomes in a blended learning course that provided the webcast recordings as the primary online tool in the course. Furthermore, this presentation will also discuss whether the webcast viewing was associated with students` final grades in the course. First I will provide a brief overview of blended learning and review different categories of blended learning that exist in today’s educational system. then I will move into the theoretical framework that I used in the study and briefly review the major components of the self-regulation model. Next I will talk about our recent evaluation of blended learning at trent university and discuss our key findings from our pilot study. With the valuable support of my supervisor, Dr. Brenda Smith-chant, I was able to develop a new exploratory model of self-regulation in a blended learning environment. This model is among the first models to examine previously unexplored dimensions of self-regulation in a blended learning settings, and I am very excited to be able to share that with you today.
- - The term “blended learning” has been used frequently across studies. In order better understand the essence of blended, it is very to important to define what blended learning really means. Blended is simply the combination of online and face to face learning. It is very important to define what we actually mean by face to face learning – f2f learning can be categorized into traditional face to face, which no technology is used, and web facilitated face to face, which is a learning that delivers 1-29% of the course content online ) – Today majority of the f2f courses would be categorized as web facilitated f2f learning as use online learning management systems such as WebCT – to provide easier access to various contents such as syllabus, lectures slides)In contrast to f2f learning, if a course delivers 80% or more of its contents online, it is considered an online course. So why combine these 2 environments? The answer is very simple, if you only use a single approach to learning new skills – it is possible that a single approach won’t help you as far as you need to go. blended learning is much more effective than online or face to face learning, since it provides pedagogical design where characteristics of online environment (e.g., time flexibility, student autonomy, reduced in class requirement) complement the characteristics of a face-to-face environment (e.g., higher quality of interaction between students and instructor, more direct feedback from instructor, and more instructor-controlled course structure (Graham, 2006; Osguthorpe & Graham, 2003; Vaughn, 2007).- Higher education institutions are less likely to select the third category due to limitations such as lack of enough class space or accessibility to modern technologies-
- When designing a blended learning course, the first question that comes to mind is “how should I blend”. It is very important for instructors to have a clear objective when introducing blended learning to students. There are three main categories of blended learning that are very well known in the field.. These categories are enabling blends, enhancing blends, and transforming blends. It possible for some blended courses to fit into multiple catetgories, but usually a blend in a particular course matches closely with one the three categories. It is important to note that none of these categories are necessarily bad, they just have different focus. 1. for the first category, the enabling blend, focus is on addressing issues of access and convenience for students. A great example for this category is format that is adopted university of phoenix, where students have the option of enrolled either in f2f, onlne, or blended courses, which is selected based on their budget and time flexibility required to successfully complete the course. select their courses (i.e., face-to-face, online,and blended) based on their budget and the time flexibility required to successfully completetheir classes.for the second category, enhancing blend, the goal is to permitthe implementation of resources and supplementary tools into the traditional face-to-faceenvironment. This is the caegory that closely matches the blended course that we evaluated at trent university, since webcasts materials were posted online as part of the online learning tools. 3. The last category is transforming blends, and the focus here is to allow a fundamental transformation of pedagogy by using the latest technologies that areavailable today. Corporate settings are much more likely to rely on third category (i.e., transforming blends), since they have access to moreresources. You would rarely see this categories in educational institutions as it is very costly.
- - As discussed before, blended learning can very effective learning solution for students as it provides pedagogical design where characteristics of online environment (e.g., time flexibility, student autonomy, reduced in class requirement) complement the characteristics of a face-to-face environment (e.g., higher quality of interaction between students and instructor, more direct feedback from instructor, and more instructor-controlled course structure.- Therefore students enrolled in a blended course are able to control the pace and location of their learning, this is specially important for students who have family and work responsibilities. Blended learning also allows students to review the material through out the course. For example, when students have access to webcasting, they have access to their professor 24/7 - which can provide a much more flexible learning for students. - However time management is a struggle for many undergraduate students and lack of time management has the potential to significantlyhinders students’ progress in a blended course. Lack of time management is specially a concern for first year students who may not have the necessary self-regulatory skills to direct their behaviour s in a less structured environment. Procrastination is not a new concept in technologically enhanced courses, student procrastination has also been a serious concern in online courses. In order to better understand the role of cognitive, behavioural, and motivational attributes in online courses, many studies have turned to the social cognitive model of self-regulation. This model is the same model that I used in my study, as it has been shown to be a very valid and reliable model in online studies.
- Before reviewing the model – it is very to define what self-regulation – self-regulation is basically the process by which learners personallyactivate and sustain behaviours that are systematically oriented toward the attainment of learning goalsForethought phase refers to processes that occur before efforts to learn, performance phase refers to processes that occur during the application of behaviour, and self-reflection phase refer to processes that occur after each learning effort . There are 2 major classes of forethought phase: which are task analysis and self-motivation beleifs, and 2 major of classes of performance phase, and 2 makor classes of self-reflection phase.
- In the forethough phase, task analysis involves goal setting and strategic planning, whereas self-motivation beleifs consists of self-efficacy, outcomes expection, intrinsic interst ad calue and learning goal orientation. Self-efficacy is basically, one’s believe in his/her ability to accomplish a learning goal.Outcome expectaiotn – is about personal consequences of learning, so if the student is going to apply to med school, he or she will be dedicated to do really well in the mcat. AND intrinsic value, refers students tendency to be driven by intrisnc rewards such as cuirosity and challenge. Learning goal orientation – is either extrinc or intrisnc. Extrinsic is when students are driven by external rewards, such as grades and recognition, where as intrnsic is when students are driven by curiosity, interest, and mastery).
- Performance phase consist of 2 major classes of self-contorl and self-observation .Self-contorl involves imagery, which is holding an image of the concept that students is interested in learning and give self instruction on how to best learn that concept. Attention focusing is self-explantory, its when students select learning settigns that are away from distraction, and task strategies, is when student uses creative strategies for learning certain concepts. Self-observation self-recording to make sure they track tey time and find out how mcuh they spend studyingSelf-experimetnation is when students attempts to improve his/her productivity by trying new strategies such as studying in groups or studying during specific times of the day.
- As discussed before, there are 2 major components of self-reflection , 1. self-judgment and self-reaction,self-evaluation, refers to comparisons of performances against some standard, such as one’s prior performance, and causal attribution refers to beliefs about causes of one’s success or failure. Self-reaction involves feeling of self-satisfaction about one’s performance on a given test. – increase in self-satisfaction will increase motivation whereas decrease in self-satisfaction will decrease motivation.Adaptive reaction refers to one’s adjustment of learning strategy by practicing a more effective strategy and defensive refers to one’s tendency to protect self-image by withdrawing or avoiding opportunities to learn and perform.
- Generally, research on self-regulation and blended learning is very limited – to date, studies have indicated that students’ success in blended learning environments is associated with four specific dimensions of self-regulation, namely, Intrinsic, self-efficacy, time and environemtnmanagemetn, and help seeking.
- Recently, the department of Psychology at Trent University revamped the format ofIntroduction to Psychology course. This introductory course was changed to become a courselevel blended approach where face-to-face instruction and online supportive tools werecombined as part of the course. The purpose of this course level model was to blend webcastingtechnology with face-to-face instructions, without significantly altering the teaching and learningexperiences in the course (i.e., “enhancing blends”). As a result, 50% of the course contents weredelivered in class (i.e., face-to-face) and 50% of the course contents were delivered online.Students could either attend the lecture or use the course management system (i.e.,WebCT) to access the recorded webcasts and all other components of the course (e.g.,assignments, quizzes, etc.). The face-to-face component of the course required students to attendfortnightly labs where they participated in hands on activity such as skill sets or techniques usedin psychology).The Introduction to Psychology course at Trent University was modified n orderto provide a harmonious balance where strength and weaknesses of the face-to-face lecture couldcomplement the strength and weaknesses of the online portion of the course (Osguthorpe &Graham, 2003). Therefore traditional environments allow students to enjoy the benefits of grouplearning where verbal comments and non-verbal cues between students and instructors canpositively influence students’ learning outcomes. The online portion of the blended learning alsoallows students to exercise more control and flexibility over their learning. This is especiallyimportant for students who may miss a few lectures due to major life responsibilities (e.g., fulltime job). The availability of recorded lectures also provides the opportunity for learningdisabled students to review the lecture materials after the lecture. Similarly, internationalstudents or students whose native language is not English could also use the available webcaststo edit their lecture notes or prepare for their exams.The webcasts are also Close- captioneds for the hearing impaired. Studnets also have accesss to the slides while viewing the lecture.
- - This study observed a previously unreported bimodaldistribution f or webcast viewing patterns. Students viewed the webcasts either immediatelyafter the lecture or a few days prior to the exams.This Figure provides an example of webcast viewing pattern from Lecture six infirst semester. This pattern, however, is remarkably consistent for every single lecture. - It is possible that the ‘immediate viewers’ werehighly resourceful and the students who viewed the exam were less resourceful. This may be areasonable assumption as student procrastination has been an important concern in blendedEnvironments- However, low self-regulators may not have been the only students to access webcasts prior to thelectures. Students with high self-regulation skills may have also accessed the lectures in order toreview specific sections of the course that they have difficulty with. Unfortunately, the limits ofthe Panopto system prohibited this exploration. Panopto was only able to generate data (i.e.,frequency and duration of viewing for each lecture) for the top 100 viewers in each lecture.Detailed information on each student’s viewing patterns may have provided a more accuratepicture of viewing patterns in the course.Students have been shown to view the webcaswts before the exam in previous studyies, but this study is among the first studies to show and immediate increase in students webcast viewings following the lecture.
- Another goal of this study was to test the proposed model of self-regulated learning in ablended learning environment. This model was among the first models to empirically examinethe role of previously unexplored self-regulatory variables (i.e., task value, effort regulation, peerlearning, and test anxiety) in a large blended learning course. Task value refers to: Task value refers to students’ attitude towards the learning materials in the course and examines whether students’ view learning materials as interesting or important (Pintrich et al., 1991). Peer learningrefers to collaborative learning and students’ tendency to study and solve problems with their peers (Pintrich et al., 1991). Test anxiety refers to emotional (e.g., pessimism), physiological (e.g., arousal), or cognitive factors that may negatively contribute to student’s test performance(Pintrich, et al., 1991). As discussed in literature review, peer learning, task value, and test anxiety tend to play a major role in traditional face-to-face learning environments (Pintrich et al., 1991, Pintrich, 2004).Due to nature of the blended course in the present study, it was expected that peer learning would be among the self-regulatory variables that would contribute to overall webcast viewing times inthe course. As discussed earlier, many students reported viewing the webcasts in groups and with their classmates. Therefore, peer learning was also added to the model. These variables along with original self-regulatory variables (i.e., intrinsic goal orientation, self-efficacy, time and environment management, and help seeking) served as the independent variables in the model. Overallviewing time and students’ final grades were the dependent variables in the model. Overall viewing time was the only variable to serve as both dependent and independentvariable. When examining the direct impact of self-regulated learning on students’ academic outcomes, one can observe that self-efficacy had the highest significant positive impact on finalgrades. This suggests that students who scored higher on beliefs about their academic abilities performed significantly better than students who scored lower on the same scale. This pattern isnot surprising as self-efficacy has been shown to be an important component of academic success in blended settings (Barnard et al., 2009; Lynch and Dembo, 2007; & Orhan, 2007).After self-efficacy, effort regulation had the highest significant direct effects on students’ final grades. Therefore, students who directed their efforts despite external distractions were able toperform significantly better than students who experienced distraction while engaged in their academic work.As observed in the model, the overall viewing time was not significant at the 0.05 level,but there is a trend for overall viewing time to be correlated with final grades. The portion of thevariability that was produced by the direct effects of self-efficacy and effort regulation reducesthe importance of the unique contribution of overall viewing time to final grades. Althoughoverall viewing time did not reach significance as predictor of grades, it is still one of the mostimportant components of the model as it served as the mediator variable in the path model. Theproposed model would not meet the required standards of a structural equation model in theabsence of the overall viewing time.In order to better understand the indirect influence of self-regulated learning on students’final grades, one needs to thoroughly examine the impacts of self-regulated learning on overallviewing time. As shown in the model, overall viewing time was predicted by the belief that thecourse was pleasurable (i.e., intrinsic goal orientation). Students who were more strongly drivenby intrinsic rewards (e.g., curiosity, challenge, and mastery) performed better than the studentswho were less strongly driven in this domain.Overall viewing time was also predicted by students’ ability to direct their efforts despiteexternal distractions (i.e., effort regulation). Another interesting finding was the negativerelation between time management and overall webcast viewing times. This pattern reflects thatstudents with lower levels of time and environment management skills were more likely to viewthe webcasts. This is not surprising as the negative association between time and environmentmanagement and overall webcast viewing could reflect the viewing pattern of students who mayhave missed lectures or procrastinated.At first glance, one may assume that procrastinators may not benefit from the webcasts.However, overall viewing time is positively related to grades. Also of note is that students whoview webcasts for longer periods of time are more likely to be intrinsically motivates. Onepossible conclusion that could explain this pattern is that students who are poor at timemanagement but who enjoy academics and have high levels of effort regulation end up watchingthe webcasts and achieve better grades than students who don’t watch the webcasts.When analyzing the final model in this study, it is very important to understand that themodel fit was significantly dependent on the presence of both significant and non-significantcausal paths. It is quite possible that the non-significant variables worked as suppressorvariables. Suppressor variable is a defined as a variable that “increases the predictive validity ofanother variable (or set of variables)” in the path model (Conger, 1974, as cited in Maassen &Bakker, 2001, p. 246).Overall, of 11 causal paths specified in the path model, 6 were not significant. Accordingto the model in this study, task value (i.e., whether students view the course material asinteresting or important) had a negative and non-significant correlation with final grades. This isnot surprising as the participants in the present study were first year students and many were notpsychology majors and took the course either as an elective or as part of their first yearexperience at Trent University. As a result, they may have also extended less effort in thiscourse as they were more focused on courses that were associated with their future major. Thistendency to focus effort on key courses of interest and not all courses may represent an adaptionto an unfamiliar university environment.A similar argument can be made for describing
- Students withlow self-regulation skills can also benefit from webcasts as long as they are driven by intrinsicrewards and the direct their efforts despite various environmental distractions