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The data science trends identified in this blog series are definitely here to stay. However, the upcoming challenges and opportunities lie in operationalizing the various educational technologies in day-to-day learning environments. This blog focuses on two such environments: credit/certificate based learning programs and higher education. In addition, we re-visit Game-Based Learning as a technique which is still evolving and where the education landscape still needs to find repeatable successes in order for the approach to gain acceptance in wider applications.
Future of Massive Open Online Classes (MOOCs):
MOOCs provided by companies such as Coursera, Khan Academy, and EdX have changed the manner in which individuals educate themselves worldwide. The basic idea behind MOOCs is that anyone can receive higher education/training at anyplace at anytime for free. These online courses have gained popularity due to the array of content made available by top-tier institutions.
In the past, individuals have not been able to receive credit for these courses due to potential identity fraud. Coursera, however, has recently broken this boundary by experimenting with keystroke biometrics in their Signature Track verification system. The system analyzes the user’s keystroke characteristics and facial image to verify user identity. This process, thereby, allows Coursera to offer their students a verified certificate for a given program to show potential employers.
Even though the use of keystroke biometrics has furthered individuals’ career paths, it has even greater potential in the organizational context. Companies are often obliged/encouraged to offer employees opportunities to expand their knowledge and skillset. These benefits are often provided by sponsoring employees to attend conferences and/or workshops. MOOCs can potentially be used as a substitute or complement to these current benefits. For example, for employees that are interested in furthering their knowledge in data science, employers may offer to sponsor their Signature Track enrollment ($30 – $100) in the John Hopkins’ Data Science Certificate available on Coursera. In order for companies’ to utilize MOOCs to their full potential, Coursera, Khan Academy, and EdX may consider partnering with industry leaders to provide more industry focused workshops.
Switching to ‘Evidence Based’ Approaches in Higher Education
Institutions in higher education are also not immune to the changes in the educational landscape brought about by online and ‘automated’ learning environments. Candice Thille, the founding director of the Open Learning Initiative (OLI) at Carnegie Mellon, foresees major shifts in the way classes are structured at traditional educational institutions such as community colleges and universities. Specifically, she predicts “a switch from an intuitive to an evidence-based approach for course development, delivery, and assessment and from a solo content expert to an interdisciplinary team for developing, evaluating, delivering, and improving courses” (Thille & Smith, n.d.).
Overcoming general aversion to risk as well as faculty distrust of the ultimate objectives of ‘automated tutors’ will require rigorous evaluation of the true impact of these types of technologies and how they are informed by learning-science theory. Organizations such as OLI and the Pittsburgh Science of Learning Center are already engaged in research of this kind where “student learning data [from adaptive learning platforms] not only provides feedback to students, instructors, and course-design teams but also prompts further research” (Thille & Smith, n.d.).
The increasing research focus in this area of education suggests that we can expect to see formalized insights from the learning sciences to inform deployment of these technologies. There are a huge array of ‘parameters’ which need to be assessed and tweaked as part of deployment. For example, one of the major research areas focus on determining the right ‘mix’ of online vs classroom content delivery across subject areas. Thus, student performance data has to be monitored and reviewed to identify where the ‘automated learning systems’ need traditional teaching support. Furthermore, if the goal is to provide ‘personalized education’, the optimal ‘mix’ parameters may need to be computed separately for each student based on their aptitude and learning styles. Recognizing these parameters would require a combination of adaptive learning data science techniques as well as domain expertise from the learning sciences.
Future of Gamification Learning
Gamification learning is still a relatively new field and it has a promising future for growth. It has the potential to grow through all academic fields and help promote learning. However, the future of gamification learning will be heavily dependent on how well games can engage the user. The most difficult problem in engaging the user is discovering a game’s difficulty sweet spot. If a game is too difficult the user will feel too frustrated, likewise if a game is too easy then the user will get bored. In both these case the user will most likely give up. Finding the right difficulty will give user a sense of achievement as they progress through the game and keep on playing. Eventually, gamification learning will elevate social learning through Mass Multiplayer Online game (MMO). MMOs are online games in which many players can work together simultaneously to complete certain tasks. The problem with the growth of MMO is that games will eventually require lots of server to group all these player into different cluster and have a good cluster technique identify what difficulty each player should play at. However, the growth of MMO might lead to a decline in personalized learning and we need to find a way to ensure that individuals will also learn on a personal level. It’s important to find the balance between independent task and group task such to ensure the growth of each individual (Li, 2013).
References
Li, Ming-Chaun, and Chin-Chung Tsai. “Game-Based Learning in Science Education: A Review of Relevant Research.” Journal of Science Education and Technology 22.6 (2013): 877-98. Print.
Thille, C., Pittsburgh Science of Learning Center & Smith, J. (n.d.). Cold Rolled Steel and Knowledge: What Can Higher Education Learn About Productivity? Retrieved April 15, 2014, from Change the Magazine of Higher Learning: http://www.changemag.org/Archives/Back%20Issues/2011/March-April%202011/cold-rolled-steel-full.html