- Crowdsourcing Higher Education: A Design Proposal for Distributed Learning
- by Michael Anderson, Director of Online Learning University of Texas at San Antonio San Antonio, TX USA Michael.Anderson1@utsa.edu
- Texas ranks 35th in the nation for graduation within six years (Austin American-Statesman, 2010, July 13).
- the cost of public higher education has risen even faster than the cost of healthcare (Langfitt, 1990). Increased costs have primarily impacted the pocketbooks of students as public appropriations for universities over the last decade have declined an average of 5.6% annually adjusted for inflation (Southern Regional Education Board, 2010). Private universities are not significantly better off.
- The combination of rising costs and perceived low performance is reflected in the public’s lack of confidence in higher education to deliver a worthwhile service (Texas Public Policy Foundation, December 6, 2010).
- While the path of instructional technology is littered with the unfulfilled promises of all-encompassing answers, a possible solution is emerging. The growing availability of low-cost computer networks, capable of linking novices and experts in social and contextual environments, reduces the inherent friction of production and elaboration in higher education. Learners no longer need travel to Cambridge to take a media literacy course from Henry Jenkins; they can watch his lecture on an iPhone™ or friend him on Facebook™ and chat about the utility of social networks. Using networks for distributed learning can solve the efficiency challenge if the academy can embed that learning in effective instruction.
- Effective instruction requires constant adjustment to the learner, reinforcing mastered concepts and holding out new concepts that are barely able to be mastered by the learner at that point in his or her concept knowledge trajectory. Computers can constantly evaluate and adjust to inputs in an efficient manner, providing personalized instruction. Computers can also track performance at a granular level and match learners with experts on the basis of fine-grained competencies for the purpose of targeted mentoring. Embedding these metrics within a networked environment facilitates the computer’s information management capabilities across multiple characteristics of learning interactions and within a computer-mediated socialized network. However, humans can better interpret a lack of understanding of the intermediate steps in a problem-solving process, and humans can offer complementary explanations easily adjusted based on feedback. Digital capture of these explanations can be archived for use by other students, and the quality of those digital artifacts can be verified by the performance of the student consumers, resulting in a collection of diverse and proven solutions.
- Crowdsourcing views humans as processing units which can be integrated with computer processors to draw on the unique strengths of each (Alonso, 2011). Crowdsourcing is not a group of people performing a task typically performed by an individual, but rather an approach that leverages the individual strengths of human and machine processing.
- Brabham (2008) argues that crowdsourcing offers a solution to complex problems that require both types of computing: human and machine, interpreting and manipulating.
- Surowiecki (2004) maintains that crowdsourcing “wisdom” requires independent, decentralized answers with cognitive variety, properties that are characteristic of a collection of solutions created and rated by individuals.
- It’s 10 pm, and Will is working on his assigned Chemistry 101 homework. He logs into his personal learning system (PLS), and the Chemistry course menu shows he left off his last session at “Balancing Equations” so he decides to tackle that topic this evening. The PLS assigns random problems from the “balancing” topic, and Will works a couple of problems correctly, then misses a couple. After working on the problem set and failing to correctly answer four consecutive questions, the PLS soon offers him the choice of watching a video or talking with another student. Will watches the video, but when he tries the problem set again, he is still unable to correctly answer four problems in a row and decides he needs to talk with someone. The system matches him randomly with Miguel, another Chemistry 101 student who is online and who has already mastered the topic. The system launches a semi-private (first names only) voice-enabled whiteboard with the last problem Will missed on the screen. Miguel talks Will through the problem and offers hints on how he (Miguel) approaches the “balancing” topic, in this case, by starting with the atom with the largest coefficient.
- If Miguel’s solution engenders Will’s success, Miguel is credited with a point on the Chemistry leader board. If Miguel accumulates enough points, he may be offered a teaching assistant job next year. Miguel’s and Will’s video session was recorded and added to the library of videos for the “balancing” topic and awarded a point if it was successful. Over time, if other students are similarly successful with the “balancing” topic after watching Miguel’s and Will’s video, the session will be publicly recognized as an effective instructional segment for the topic and will rise to the top of recommended content objects for that topic.
- Vygotsky determined that learning occurs primarily through social mechanisms. Wertsch & Sohmer (1995) trace two additional themes from Vygotsky’s work: the requirement of a coach (or “more knowledgeable other”) and the identification that learning occurs in the “zone of proximal development,” the region between the learner’s ability to perform a task under the guidance of a coach and the learner’s ability to solve the problem independently.
- Lave & Wenger (1990) added the role of environment by showing that learning occurs in contextual situations, evolving from “legitimate peripheral participation” to full participation in an authentic community of practice.
- John Seely Brown (1989) extended situated learning to emphasize the role of cognitive apprenticeship.
- The crowdsourced PLS is based on social learning experiences embedded in an authentic “just in time” community of learning: the online mentor provides apprenticeship, and the dynamic menu continues to increase the depth of the topic to the level needed by each student individually (for example, an Engineering major needs more depth in Calculus than a Journalism major).
- a distributed learning network. An online system that combines the motivation of personal goal achievement with the socialization aspects of peer mentoring offers an effective solution.
- Distributed work teams and community evaluation and guidance have emerged as accepted methods for solving problems over geographical distances (Resta & Laferrière, 2007).
- open educational resources provide course materials for direct access and reuse. Learners are encouraged to utilize and in some cases, modify and share improvements in a content collaboration similar to Wikipedia.
- Extending the open content model, the University of Manitoba offered an open online course in 2008 which enrolled more than 2,300 students (Fini, 2009). The PLS solutions library functions as an open resource, at least for that class at a specific institution, with diverse content which can be directed to students on the basis of demographic data, learning patterns, and other performance metrics captured and indexed by the crowdsourced PLS.
- Jenkins (2006) argues that students are digital residents who live in a participatory age. Participation in the content construction aspect of learning environments is often characterized by the use of blogs or discussion boards that ask student to analyze and summarize core readings in a discipline and encourage (or require) peer responses to those posts. The pedagogical affordance of analysis by each student reduces faculty member workload by shifting the responsibility for knowledge acquisition to each individual learner. The formalization of blogs and other student-created content is realized in the advent of student portfolios.
- The crowdsourced PLS situates students directly in the learning environment at the moment of need and relies on human communication to interpret complex problems. Faculty workload is reduced through the engagement of external mentors (in real-time or as a content resource from the stored sessions) who fill the role traditionally assigned to discussion leaders. Situating these communications in virtual environments, whether fictional (Liu, 2006) or real (Doering, Scharber, Miller, & Veletsianos, 2009), provides intrinsic motivation for learners to remain personally engaged.
- Table 1. Instructional Design Approaches Approach Advantages for learners System disadvantages Collaborative projects Socialization within group Potential for no individual accountability Supplemental Instruction (SI) Socialization within group Content placed in immediate problem context User-initiated (not embedded) Open Educational Resources (OER) Socialization within group User control Lack of personal direction Public content Opportunity for reflection Motivation from participation Absence of assessment and feedback Peer assessment Motivation from participation Possible system manipulation Problem-Based Learning (PBL) Motivation from participation Socialization within group Content placed in immediate problem context Requires external experts Serious games Motivation from participation Socialization within group Content placed in immediate problem context Intrinsic motivation for learners and mentors Expensive to develop
- students must accept communal responsibility and provide mentoring in a quid pro quo environment where payment is non-material. Empowered by proven techniques in social learning design and crowdsourcing, these new responsibilities promise more effective and efficient learning outcomes.
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