Big Data and Models of Innovative Teaching with Cognitive Computing 1
2
M. Coccoli, A. Guercio , P. Maresca , L. Stanganelli
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DIBRIS, University of Genoa Via Opera Pia, 13 I-16145 Genova, Italy
[email protected] 1 Kent State University – Stark 6000 Frank Ave, NW, Canton, OH 44720
[email protected] 2 DIETI, Federico II University, Naples Via Claudio 21, 80125 Napoli, Italy
[email protected] 3 Università e-Campus, Via Isimbardi 10 22060 Novedrate (CO), Italy
[email protected]
The paper discusses the smarter university model and some experimental activities as the base model that supports the educational institution while transitioning from the information technology era to the knowledge technology era. Jobs that require news skills and abilities will be in demand and the academy will be transformed from the role of "generator of knowledge" to the role of "generator of knowledge generators".
1. Introduction As MIT professors Erik Brynjolfsson and Andrew McAfee state in their latest book, we are at the dawn of the second machine age, an era in which we see machines that we did not believe were possible to create only a few decades ago. The machine evolution will continue because “the exponential, digital, and recombinant powers of the second machine age have made possible for the human to create two of the most important one-time events in our history: the emergency of real, useful artificial intelligence (AI) and the connection of most of the people on the planet via a common digital network. […] In this new age we care more about ideas not things, minds not matter, bits not atoms, interactions not transactions […], which call for new organizational structures, new skills, new institutions, and perhaps even a reassessment of our values.” [Brynjolfsson and McAfee, 2014]. The invention and the use of such machines consequently require the creation of specialized skills. Never like now, there has been an increased demand for skilled labor. Many economists have confirmed this trend and they DIDAMATICA 2015 – ISBN 978-88-98091-38-6
called it “skill biased technical change” which favor people with more human capital. In this context education plays a role more important than ever before. On the base of this important analysis many questions arise. Here are just a few: “What types of skills should the people have?”; “Which institutions can help in the skills formation?”; “What changes in the university should be performed in order to prepare the future workforce?”; “Which model can be set in place or how the previous model can be modified to help transform the university in presence of big data?”; “In which way the universities will attract new customers by recombining big data and skilled labors?”. All these questions can be summarized in a single million dollar question: “What should be done to improve the welfare of the people in the second machine age?”. In this paper we attempt to answer some of these questions.
2. The Smarter University Model In our previous research we recognized the need of a great change in the educational area and we proposed a model of a Smarter University [Coccoli et al, 2014]. The name “smarter” derives from the fact we believe that, in general, universities are smart but they should become “smarter”. We recall the model of a smarter university in Fig. 1. The process is made of several steps: Opinion mining – collecting different opinions, which will be later organized and structured. Needs collection – an in-depth analysis of the needs emerging from the area, which are organized according to their sources (stakeholders). These views are then translated into specifications and constraints of the system.
Fig. 1 – The smarter university model
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Vision – the presence of multiple variables and constraints encourages the creation of a “strategic” vision that must be translated into clear objectives, ambitious yet realistic and, most important, measurable. Thus, we define a set of goals G = {Goal1, Goal2, ..., GoalN}, whose reach is measured through the Key Performance Indicators (KPIs) set K= {KPI1, KPI2, ..., KPIN}. Priorities – the objectives are ordered as a two-dimensional array according to their priority. Common contents – the model highlights contents, knowledge and skills that an individual must have in multiple scientific areas, corresponding to the transverse part of T-shaped people. Domain specific contents – the vertical part of the T is represented by the knowledge and the skills that individuals must possess in a specific domain. For these domain-specific contents suited vertical courses are identified. Competences, standards and policies – on the right part of Fig. 1, the competencies are described and teachers’ skills are taken into account. Matching – one of the most challenging parts of the model is matching the choices with the needs. The complexity is due to both the presence of requirements imposed by outside the system, and to the feasibility of the actions defined in the higher parts of the model. Monitoring and analytics – the model provides an abstract representation of a vision. Its application to specific situations requires the implementation of a specific structuring of each part and such a process that can be very complex. Moreover, this model may require forecasting, simulation, data collection and analytic tools. The presented vision of a smarter university is the vision of the future university, which responds to the students’ needs in a sustainable, social and technological way. “Being smart” should not be confused with “being digital”. For example, the availability of broadband as a resource is essential to ensure that business is more competitive and to reduce the digital divide between citizens; the availability of low-frequency short-range technologies represent an essential resource for enabling the development of the IoT [Sissions, 2011]. Similarly, Wi-Fi technology provides a valuable tool for reaching high-frequency strategic areas such as laboratories, libraries, meeting points, and so on. Of course, wherever the interconnection of these technologies is available, it might be possible to have an effective and timely monitoring, as well as a constant update, of each student’s part of the vision.
3. Pondering on the Smarter University Model While looking at the proposed model, we realize that additional aspects have to be contemplated. The first question we like to ask is “What are the major elements that speed up the knowledge process?”. In a recent paper we have DIDAMATICA 2015 – ISBN 978-88-98091-38-6
shown how the performance of a student grows significantly if within the educational university a training course held by a company is grafted [Coccoli et al., 2015]. In addition, a set of experiments has been carried on in order to validate our model. The observations from the initial experiments described in [Maresca et al, 2014] have been used to create a more advanced experiment in which the IBM BlueMix platform has been used to prepare students in a pilot course at the Faculty University of Naples. The preliminary results collected from this latest experiment, named ETCBLUE, demonstrate that the use of the IBM BlueMix platform, tacked on the complex eco system based on the previously developed Eclipse and Jazz, can greatly improve performances of the students. We observed that students gain core competences faster and in a realistic working environment. We also note that the most important part of the architecture is the collaboration (smart ETCBLUE), which made possible the design and development of the resources useful to the team. Moreover, ETC-BLUE drives standards, forces open innovation network, requires mature organizations, and produces high quality products. ETC-BLUE fosters learning methods that are student-led versus instructor-led, with professors playing a mentor role. This learning model implements a student centric paradigm, which constitutes the basis for collaboration between people within a team and among groups. We note that while observing learning in collaborating groups, we can say that some groups are crawling, others groups are walking, others running and others flying. Identify groups that fly and motivate other groups, means to identify the elements that help achieve faster results all across the spectrum. This means that activities like ETC-BLUE can nurture the creation of smarter campuses. Smarter campuses are interconnected, enriched, and fed by the ground knowledge developed and spread across the social networks [Coccoli et al., 2015]. ETC-BLUE favors the creation of smarter universities and forces teachers to have the most updated and relevant curricula which, consequently, tend to attract better students who, in turn, will receive the best formation - a virtuous circle generated by the collaboration between universities and companies. Applications of activities, like ETC-BLUE, implement team-based projects across geographical, disciplinary, and institutional boundaries while sustaining a community that enable to form “T-shaped” students. Finally ETCBLUE fosters leadership and e-leadership. The second question that arises is how do we transform the smarter university model in presence of big data, and how will smart universities attract new users by combining big data and new skills? Probably the cooperation between academy and industry will help achieve such a goal. In this new era the role of the academy will be different and the future university will go from the role of "generator of knowledge” to the role of "generator of generators of knowledge ": a vision of a "meta-model" (as metamodel is the smarter university model shown in Fig. 1) in which universities,
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organizations, and companies all together learn from each other consequently becoming "smarter" and more "cognitive". The knowledge must be generated in a more intelligent way and the new professions will become increasingly cognitive and require new skills. Moreover, moving from the information technology era to the knowledge technology era is accompanied by the fact that there will be (as there it is already clear now) an enormous set of data from which we must extract knowledge. Perhaps the new professions will become more interesting and will attract more young people in studying at a university and in participating in various courses - delivered also in the spirit of ubiquitous E-learning [Arndt and Guercio 2014] - that will address knowledge and knowledge extraction from big data. Companies that need to grow and think about replacing many of their employees might be interested in reconverting them (instead of firing them - see IBM) in areas that are challenging and driving. The universities should be prepared for this challenge by creating and or offering degrees for the formation of "data scientists". In all this, we think that the cooperation between universities and industries is still the key of these new times.
4. Big Data, new Professions, and new Teaching Models The digitization will change the teaching and learning models. The changes will have an even greater impact on the social life because the professions will change and the changes are already visible. The new jobs will require new skills by changing many social assets, since old jobs will disappear while new ones will be created! As a consequence the teaching and learning models must be changed to produce the required skills. Many universities start from a traditional model in which teachers generate knowledge through research. They disseminate knowledge to the students at their universities either face-to-face or in distance learning mode. Then the students apply that knowledge in the real world after gaining the proper qualifications according to the level of acquisition. Unfortunately, this model is already old.
Fig. 2 – The knowledge generation process - [Orallo, 2014].
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Companies, individuals, as well as industrial and educational organizations are aware that we are in the midst of a profound transformation. This transformation is perceived from the ocean of digital data that are generated every day (see Fig. 2). It is natural to think that these data can be used to extract knowledge in very specific domains. See for example, the experiment that IBM and the Memorial Sloan-Kettering Cancer Center of the Cleveland Clinic is carrying out in the attempt to create a new astonishing machine that augments the human abilities to identify and diagnose diseases in patients. In the experiment Watson has been made capable of consulting a bank of medical data to produce accurate diagnosis of patients. Watson is not intended to replace the competencies of the human doctor, neither to substitute the human medical diagnosis and his and her deduction abilities, rather to augment the human abilities with its high search abilities of big data and automatic deductions that can only be provided by a computer. This means that in the near future many of us will may be paired in their jobs by a machine, thus requiring to have acquired skills to extract from the machine the proper information that augment our knowledge and help do a higher quality job. At this point there are two fundamental problems we need to observe. The first one is that the data are growing too fast and that the social usability of those data decreases with the time. Indeed we need new professional skills that can extract data from very specific domains, often complex data. As the Google’s Chief Economist Hal Varian says, as reported in an article of the New York Times by S. Lohr: “I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding.” [Lohr, 2009]. The second one is that we do not know the best model for the formation of data scientists, knowledge engineers, knowledge managers, and so on. An estimate [Mc Kinsey 2012] says that in the next year in the US alone will need a million data scientists and we know that the universities are not prepared for this challenge. The arrival point, when the transition from one era to the other will be over, will be a new educational paradigm in which the model will be totally centered on Big and Open data. We believe that the process model is likely to be the following, depicted in Fig. 3:
Fig. 3 – The new educational process - [Orallo, 2014].
i.e., a model in which cognitive systems will have the ability to extract knowledge automatically or semi-automatically from application domains. These cognitive systems will also be able to build useful knowledge for tomorrow's Genova, 15-17 Aprile 2015
jobs. In short, users will be interacting with cognitive systems directly on a daily base. Therefore they must have a proper formation to “speak” with such systems. When this point will be reached, the knowledge generation will be more fast and efficient. The knowledge transfer in the social world will be the fundamental key for successful business. The new jobs, beyond statistician, as Valerian predicts, will be cognitive system developers, integrators, evaluators, and trainers. These are all jobs that require skilled people.
5. The Skills Produced by the Smarter University The skills that should be formed by a smarter university are: a. Multi-disciplinary - the new professionals need “an integrated skill set spanning from mathematics, machine learning, AI, statistics, databases, and optimization, along with a deep understanding of the craft of problem formulation to engineer effective solutions” [Orallo, 2014]. b. Multi-domain - the new professionals must be “aware of the socioeconomical context in areas such as medicine, energy, environment, finance, transportation, etc.” [Orallo, 2014]. c. Multi-empathetic - the new professionals will need to interact with: § Humans, as data/knowledge generators/consumers. § Cognitive systems, which will be taught, not programmed. § Machine-augmented humans who are assisted by devices and gadgets. § Robotic systems that replace old human jobs or help people to do it. § Hybrid groups of the previous listed bulleted points. d. The communication and interaction changes between people and things - the social media will change; messaging cognitive interfaces will change. Consequently will need skills to dialog with interfaces rather than programming them. This will produce the development of a new generation of apps named cognos. Skilled personnel that are a hybrid of data hacker, analyst, communicator, and trusted adviser seem to be a realistic need. More enduring will be the need for data scientists to communicate in languages that all their stakeholders understand. People who demonstrate special skills in storytelling with data, whether verbally, visually, or - ideally - both will be aimed [Davenport and Patil, 2012]. We agree with their observation. But the challenge is not only in the identification of the type of skills we need to provide, it is also essential to identify and find both the proper ways to produce them, and the stakeholders involved in the process.
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6. Conclusions and Future Work There is no doubt that the university must be in charge to produce new skilled figures in the knowledge technology era in which we are transitioning. At the current pace we are already in a long standing deficit. To turn the deficit into surplus we need to set in place a model that supports the educational institution in this new challenge. The smarter university model is the base and some experimental activities performed around the model discussed in this paper seem to confirm that we are proceeding in the right trajectory. The initial results show that collaboration is the base for such transition and that a more tight cooperation between academia and industry will help achieve such a goal. While still central, the role of the academy will be different with respect to the past, and will be transformed from the role of "generator of knowledge" to the role of "generator of knowledge generators". Currently we are continuing our research with ETC-BLUE. The use of the cloud has solved a lot of problems for the computer laboratories of our universities by eliminating the installation of numerous software packages, and additional technical maintenance.
References Arndt, T., Guercio, A., Ubiquitous E-Learning: Student Attitudes and Future Prospects. GSTF Journal on Computing,. 4, 1, Dec. 2014, 77-82. Brynjolfsson E., McAfee, A,. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W. W. Norton & Company, 2014. Coccoli, M., Guercio, A., Maresca, P., Stanganelli, L., Smarter Universities - A Vision for the Fast Changing Digital Era. Journal of Visual Languages and Computing, 25, 6, 2014, 1003–1011. Coccoli, M., Maresca, P., Stanganelli, L., Developing collaboration skills in a distributed cloud computing environment, IBM Cloud Academy Conf., 2015 (in press). Davenport, T. H., Patil, D.J., Data Scientist: The Sexiest Job of the 21st Century, in Harvard Business Review, Oct. 2012, 70-76. Lohr, S., For Today’s Graduate, Just One Word: Statistics, New York Times, Aug 5, 2009, http://www.nytimes.com/2009/08/06/technology/06stats.html?_r=2& Maresca, P., Guercio, A., Stanganelli, L., Arndt, T., Experiences in Collaborative Learning, Journal of e-Learning and Knowledge Society,10, 3, Sept. 2014, 121-145. Mc Kinsey & Company, State of human capital 2012, http://bit.ly/1Ca2HiU Orallo, J.H., Academic degree in data science and cognitive computing are we coping with demand? Cognitive system for smart business and computing, IBM EMEA Academic Days 2014. Sissions, A., More than making things, a new future for manufacturing in a service economy, London, UK, The work foundation, Lancaster University, 2011.
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