Video-based learning ecosystem to support active learning: application to an introductory computer science course
© The Author(s). 2016
Received: 25 May 2016
Accepted: 26 June 2016
Published: 7 July 2016
The systematic use of technologies in order to orchestrate learning has become widely used in the past years. Diverse technologies have been applied in a variety of teaching practices; for instance learning tools which allow you to flip the classroom or monitor other active learning practices. However, the developed systems are only a subset of different kinds of learning materials and learning tools that an educator should take into consideration; and most importantly they do not offer an overview of the different learning dynamics. The development of a learning ecosystem framework, which will allow us to describe “the complex of living organisms” as well as their interrelationships, will help us to better understand and further develop our teaching approaches. In this paper, we present a video-based learning ecosystem framework and the first captured results of its application in an introductory computer science course. The framework incorporates basic e-learning tools and traditional learning practices, making it accessible to anyone wanting to implement a video-assisted project-based experience in his/her course. Its application is based on open and easy-to-use tools, allowing for the incorporation of any additional functionalities. This work aims to provide insights for other scholars and practitioners to further validate, examine, and extend the proposed framework. This approach can be used for those interested in incorporating project-based or flipped classroom approaches in their teaching, since it is a flexible procedure that may be adapted to meet their needs.
KeywordsLearning ecosystem Active learning Project-based learning Learning environments Learning dynamics Video-based learning
Traditional lecture style is a common teaching approach in higher education classes; however, the traditional lecture style of teaching can often place students in a passive role, which typically involves students retaining isolated facts that can later be forgotten. Following Bligh (2000) definition, traditional lecture style is a “continuous exposition by the teacher”; in a traditional lecture instruction students’ activity is limited to taking notes and asking questions to the instructor. Over the last few years, instructors have been moving away from the traditional lecture style by implementing more active learning practices, like project-based learning and flipped classroom, and increasing the technology use as a way to extend and enhance students’ understanding.
Active and problem-based learning activities are founded upon a constructivist theory and traditional lecturing derived from direct instruction methods is founded upon behaviorist ideology. Active learning is a model of instruction that focuses the responsibility of learning on learners. It was popularized in the 1990s by its appearance on the Association for the Study of Higher Education (ASHE) report (Bonwell & Eison 1991). During the last years, active learning practices like project-based learning and flipped classroom have gained prominence worldwide, however sometimes we face a lack of consensus on what exactly active learning is. A working definition of active learning, derived from collecting opinions from 338 experts is the following: “Active learning engages students in the process of learning through activities and/or discussion in class, as opposed to passively listening to an expert. It emphasizes higher-order thinking and often involves group work.” (Freeman et al., 2014). Although this definition is quite generic, it perfectly portraits the rationale of active learning, without being restrictive. Active learning has been deployed in a number of education studies, however a framework describing the learning dynamics is typically not described; the development of a learning ecosystem framework to support active learning, will allow us to better understand and further develop teaching approaches enhancing students’ dynamics and needs.
A learning ecosystem has been described as a means for orchestrating a variety of learning approaches given by the varied characteristics of learning processes (Siemens, 2003). Learning ecosystems have seen as environments which are “consistent with (not antagonistic to) how learners learn.” (Giannakos et al., 2016), focusing on the learning process and take into account learners’ characteristics, needs and the potential dynamics and interactions with different actors (students, educators), as well as the learning environment and resources. Thus, the concept of learning ecosystem provides an ideal ground to orchestrate multiple tools and practices in the best possible way (Dillenbourg & Jermann, 2010).
Today, diverse technologies have been applied in a variety of active learning practices. However, isolated use of different technologies does not offer an overview of the different learning dynamics. Developing a learning ecosystem framework, which will allow us to describe “the complex of living organisms” as well as their interrelationships, will help us to better understand and further develop our teaching approaches. In this work we work towards this direction, by the following twofold contribution, first we present a conceptual framework of a learning ecosystem which can host active learning instruction and second we provide some first analytics-based evidence regarding its effectiveness and acceptability.
Building upon existing technologies and practices like video-assignments, clickers and micro-project approach, in the next section we propose a learning ecosystem for active learning. In the third section we present an empirical study following the proposed approach, were by collecting diverse-sourced data, we portray students’ experience throughout the course of the semester. The last section of the paper draws conclusions and discusses ideas for further research in the area. This study aims to provide a springboard for other scholars and practitioners to further examine the efficacy of this specific blended learning approach. Our conceptual framework is a flexible procedure that can be utilized and adapted to meet different needs.
Learning ecosystem for active learning
The development of the framework emphasizes in the generic view of the learning ecosystem, hence it is possible for an individual to apply it to any active learning situation, such as project based learning, or peer instruction. Another important aspect is to assert that the interrelationships and interactions with all the organisms, external influences as well as the infrastructures of the learning ecosystem are in principle dynamic (Fig. 3). This generic view helps to get a better picture about a specific learning situation, and allows educators and practitioners to achieve a more holistic approach for the development of more effective learning. A graphic representation of this definition is shown in Fig. 3.
An initial empirical validation of the proposed framework took place in a public university. The goal of this empirical validation is to provide the first analytics-based evidence regarding the effectiveness and acceptability of the proposed framework. The early results should not be seen as a rigorous evaluation of the proposed framework, but as reflections rising from a particular case study as well as empirical evidence for further development of the framework.
In order to engage students deeply in the process of learning, they worked with a group project throughout the semester and were asked to apply the obtained knowledge as well as to make progress presentation and get feedback. This gave them the opportunity to be involved in an active learning process. The aim was to engage students more deeply in the process of learning course material by encouraging critical thinking and fostering the development of self-directed learning. Active learning affords the opportunity for application and practice, and the asking of questions. During the team micro-projects, students had to find a client and mimic the professional software development process. In particular professional software development projects had the following sequence of phases: requirements, design, implementation and testing.
The empirical evaluation was conducted in two identical classes (in terms of learning goals, teacher, teaching method and so on), those classes had 510 computer science students (20–29 years old, 105 females and 405 males) enrolled in the web technology course. The course lasted 12 weeks, and we applied the proposed framework (as described in section 2.2). In addition to students’ analytics obtained from the aforementioned systems, we employed a post survey. A total of 73 students’ (14.31 %) volunteered to participate on the survey (14 females, 59 males, with mean age 22.38 S.D. 2.50).
Students’ video navigation (collected via a video learning analytics system (Giannakos et al., 2015), log-files),
Students’ learning performance/score (collected via the quizzes (Wang, 2015), log-files) and,
- c)Students’ attitudes toward the course (collected via the post survey, see Table 1)Table 1
The measures and its definitions
# of questa
The degree to which a student believes that the teaching approach of the course was easy for him/her
Ngai et al., 2007
The degree to which a student perceives how easy or difficult it would be to perform an operation in the course.
Giannakos et al., 2015
Intention to Participate
The degree of students’ intention to participate in similarly developed courses in the future.
Lee et al., 2009
The degree to which an individual believes that this teaching approach is useful.
Ngai et al., 2007
The degree to which the teaching approach is perceived to be enjoyable. [single question measure]
The degree to which the teaching approach is perceived to be exciting. [single question measure]
The degree to which the teaching approach is perceived to be boring. [single question measure]
In particular, video navigation was captured based on students’ interaction with the video player. Students learning performance was collected in pre-middle-post measures throughout the semester. The survey included multi questions factors of 1) ease, 2) control over the course, 3) intention to participate in similarly made course, and 4) usefulness of the overall teaching approach as well as the single question factors of 1) enjoyment, 2) excitement and 3) boredom. Table 1 lists the operational definitions and the number of items (questions) of each of the constructs (measures), as well as the source from which the multi question measures were adopted. We employed a 7-point Likert scale anchored from 1 (“completely disagree”) to 7 (“completely agree”).
As aforementioned, the collected data consists of three different types; therefore, an appropriate data analysis was used for each different set of data. Students’ video navigation was analyzed with aggregated time series visualizations, in order to identify students’ navigation throughout the video lecture and the importance of having a concrete assignment alongside with the video lecture. To do so we employed the socialskip.org video analytics system (Giannakos et al., 2015).
As for students’ learning performance, we captured students’ pre-mid-post assessments scores mapped them in a diagram and employed an Analysis of Variance (ANOVA), this will allow us not only to capture students’ performance toward the course, but also to identify any potential shift during the course. Last but not least we used descriptive statistics on students’ attitudes towards the course.
Students learning performance in pre, mid and post assessments
Correlations Pearson’s correlation coefficient between factors (n = 73)
Discussion and conclusions
Practical principles and heuristic models that enable actors within the learning ecosystem to understand and shape their learning future, is considered as one of the cornerstones of smart learning environments (Kwok, 2015). In this research, we presented a learning ecosystem framework and the first captured results of its application. The framework can be put into practice using basic e-learning tools and active learning practices; hence the framework can be used for those interested in incorporating project-based principles as well as flipped classroom or any other active learning approach in their teaching, since it is a flexible procedure that may be adapted to meet different needs.
By exploring the notion of learning ecosystem, we shown that is applicable to describe and model the main actors, diverse resources and sociotechnical dynamics within learning. Hence, the concept of ecosystem is an interesting approach which can be applied in learning and give an overview of the different roles, processed as well as learning dynamics.
The proposed framework was applied in an introductory course in web technologies. In the empirical study, we investigated students’ content navigation, learning performance and attitudes. We also indicated that the main quality of the “attractive” information video-segments is the rich and useful amount of transferred information and knowledge, and of course its association with students’ assessment. Last but not least, we presented students’ progress throughout the course using pre-mid-post assessment, and examined their attitudes regarding easiness, usability, usefulness, and acceptance of the course as well as the two positive and one negative emotions.
We want to emphasize that our findings are clearly preliminary with inevitable limitations. As for the internal validity of the empirical study, data are based on a self-reported method, log-files and assessments. Other in-depth methods such as semi-structured interviews could provide a complementary picture of the findings through data triangulation. As for external validity, the subjects were computer science majors, which may somewhat limit the generalizability of our results. Nevertheless, the insights drawn are not connected with the subjects’ background and can be applied on any population.
Another direction of research is to apply the framework proposed in this paper to the investigation of existing highly engaging learning practices, such as peer instruction and inquiry-based pedagogy. Our future research will concentrate on further refinement of the proposed framework by applying and evaluating it on classes of larger scale and different topics. Further research will also inform the development and evaluation of e-learning environments to better support active learning and livable and sociable learning spaces (Kwok, 2015). This study can provide a springboard for other scholars and practitioners to further examine the efficacy of this specific approach to learning, since it is an established flexible procedure that can be used and adapted to meet the needs of those interested in using the flipped classroom approach.
The authors wish to thank the students of the study who kindly contributed their time and effort. We also thank the reviewers for their helpful comments and recommendations. The paper is an extended version of the paper “Toward a Learning Ecosystem to Support Flipped Classroom: A Conceptual Framework and Early Results” presented in the 2nd International Conference on Smart Learning Environments (ICSLE’15), Sinaia, Romania. This work was partly supported from The Research Council of Norway (RCN)) under the project FUTURE LEARNING (number: 255129/H20).
All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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- T. Anderson, Modes of interaction in distance education: Recent developments and research questions. Handbook of distance education, 129–144 (2003)Google Scholar
- R.M. Bernard, P.C. Abrami, E. Borokhovski, C.A. Wade, R.M. Tamim, M.A. Surkes, E.C. Bethel, A meta-analysis of three types of interaction treatments in distance education. Rev Educ Res 79(3), 1243–1289 (2009)View ArticleGoogle Scholar
- J.L. Bishop, M.A. Verleger, The flipped classroom: A survey of the research. In ASEE National Conference Proceedings, (Atlanta, GA, 2013)Google Scholar
- D.A. Bligh, What’s the Use of Lectures? (Jossey-Bass, San Francisco, 2000)Google Scholar
- C.C. Bonwell, J.A. Eison, Active Learning: Creating Excitement in the Classroom. 1991 ASHE-ERIC Higher Education Reports. ERIC Clearinghouse on Higher Education, The George Washington University, One Dupont Circle, Suite 630, Washington, DC 20036–1183 (1991)Google Scholar
- V. Chang, C. Guetl, E-learning ecosystem (eles)-a holistic approach for the development of more effective learning environment for small-and-medium sized enterprises (smes), in Digital Ecosystems and Technologies Conference (IEEE Press, New York, 2007), pp. 420–425Google Scholar
- Encyclopedia Britannica, Ecosystem, Encyclopedia Britannica, Inc. last retrieved Jan. 21st, 2015. (2011), http://www.britannica.com/EBchecked/topic/178597/ecosystem. Accessed 2 July 2016
- S. Freeman, S.L. Eddy, M. McDonough, M.K. Smith, N. Okoroafor, H. Jordt, M.P. Wenderoth, Active learning increases student performance in science, engineering, and mathematics. Proc Natl Acad Sci 111(23), 8410–8415 (2014)View ArticleGoogle Scholar
- M.N. Giannakos, K. Chorianopoulos, N. Chrisochoides, Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. Int Rev Res Open Distributed Learn 16(1), 260–283 (2015)Google Scholar
- M.N. Giannakos, J. Krogstie, T. Aalberg, Toward a Learning Ecosystem to Support Flipped Classroom: A Conceptual Framework and Early Results. State-of-the-Art and Future Directions of Smart Learning (pp. 105–114) (Springer, Singapore, 2016)Google Scholar
- P. Jermann, Technology for classroom orchestration. New science of learning (pp. 525–552) (Springer, New York, 2010)Google Scholar
- A. Kleftodimos, G. Evangelidis, Exploring Student Viewing Behaviors in Online Educational Videos, 14th International Conference on Advanced Learning Technologies (ICALT), 367–369. (2014) doi 10.1109/ICALT.2014.109
- L.F. Kwok, A vision for the development of i-campus. Smart Learn Environ 2, 1–12 (2015)View ArticleGoogle Scholar
- B.-C. Lee, J.-O. Yoon, I. Lee, Learners’ acceptance of e-learning in South Korea: theories and results. Comput Educ 53(4), 1–44 (2009)Google Scholar
- E.W.T. Ngai, J.K.L. Poon, Y.H. Chan, Empirical examination of the adoption of WebCT using TAM. Comput Educ 48(2), 250–267 (2007)View ArticleGoogle Scholar
- M. Prince, Does active learning work? A review of the research. J Eng Educ 93, 223–232 (2004)View ArticleGoogle Scholar
- J. A. Ruipérez-Valiente, P. J. Muñoz-Merino, C. D. Kloos, An architecture for extending the learning analytics support in the Khan Academy framework. In: Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality (TEEM '13), Francisco José García-Peñalvo (Ed.) (ACM Press, New York, USA, 2013), pp. 277–284. http://dx.doi.org/10.1145/2536536.2536578
- S.B. Shum, R. Ferguson, Social Learning Analytics. Educ Technol Soc 15(3), 3–26 (2012)Google Scholar
- G. Siemens, Learning Ecology, Communities, and Networks - Extending the classroom, elearnspace, last retrieved Jan. 21st, 2015 (2003), http://www.elearnspace.org/Articles/learning_communities.htm. Accessed 2 July 2016
- A.I. Wang, The wear out effect of a game-based student response system. Comput Educ 82, 217–227 (2015)View ArticleGoogle Scholar
- H. Zhang, K. Almeroth, A. Knight, M. Bulger, R. Mayer, Moodog: Tracking Students’ Online Learning Activities. J Interact Learn Res 21(3), 407–429 (2010)Google Scholar