We all know that deep learning is quite prevalent these days from the name of machine learning.
It is typical now for corporate sector, industries to employ deep learning to solve critical problem they face for improving their business and upgrade it to the next level. It seems that those companies who will not leverage the usefulness of this new field will suffer certainly. Hence, nowadays, educationists, educational researchers and education technology companies have initiated to apply deep learning to revamp education and more specifically online learning, but, the process of its application is slow than in other sectors. Though, it has the potential to transform teaching and learning, however, it is still unsure how to gear it up in education and perhaps, a stronger and a new philosophy or a theory is much needed here.
We have already utilized technology for K12 and university education to improve learning outcomes, but, nevertheless technology has not brought better results comparatively traditional methods of teaching. Atchley (2013) found the rate of online course completion is still low than traditional face-to-face course and it was analyzed by Atchley, Wingenbach and Akers (2013) that 23% of students did not complete online course than 18% of students of traditional course. It was implied by Croxton and Rebecca (2014) that it is because of low student-teacher interactions and interaction is important for persistence in an online course. Low interactivity indicates little collaboration between student and teacher and hence, they do not immerse in deeper learning whether the learning is in class or online and it is also perhaps on account of their low engagement with learning content or it is not linked to their interests or aspirations, therefore, students do not learn deeply. Deeper learning is one of the major factor which influence online course as students are a receiver of knowledge in an online learning, so they are less interactive, less active, less engaged and thus, drop out from online course and experience low outcomes. Deeper learning entails that learners apply what they have learned to resolve novice problems by exhibiting deeper learning competencies such as collaboration, communication, critical thinking, learning how to learn and academic mindset.
American Institute of Research (2016) found that students who attended schools worked on developing deeper learning competencies experienced improved outcomes than students who did not attend such schools. Now the question here is how to improve deeper learning skill of students when they are taught through web-enabled learning and how deep learning can help in achieving it? Project-based learning, internship programs, study groups, group work and long-term assessment strategies have been effectively used by network schools to improve deeper learning competencies of their students (American Institute of Research, 2014). Apart from these strategies, one important way is to transform learners from knowledge receivers to knowledge creators, engage them with learning, enhance their level of meta-cognition, transform them from listener to a complete owner, creator, or innovator and a learning leader. Despite, engaging online content is built by instructional designers and teachers based upon improved instructional or learning philosophy, nonetheless, learners drop out from the course and do not show significant improvement. There is a need to rethink about a new philosophy of learning and bring an improved method which can upgrade students’ learning process through technology and deep learning.
User Innovation or Lead User is a method which is employed by companies to develop breakthrough product. However, the product is built by consumers rather than producers or manufacturers. Likewise, educationists can take advantage of user innovation to improve learners’ deeper learning skill. Lead user method emphasizes on collaborative inquiry, learning by doing and trial and error theories of learning. It is initiated by volunteer users (learners) who identify latest trends and needs in a topic of their interest. Here deep (machine) learning help students in gathering the recent trends. Then a cluster of lead user is formed where other people from the community apart from students and teachers are recognized who can contribute to bring useful solution to the problem along with students. A problem is framed and an inquiry question is developed by lead user cluster with respect to the recent trends identified. Then people from the cluster co-create and innovate by working together based upon collaborative inquiry, learning by doing and trial and error theories to develop new knowledge and improve their deeper learning competencies. During creation, user cluster identifies sources of information from internet or from experts in cluster which can assist them in resolving the problem of their inquiry question. The facts related to the issue are gathered. All members of user cluster later engage themselves in analyzing the facts from the gathered information and recognize patterns in the data to make inferences or conclusions. Users come together to document their understanding and solve a problem or inquiry stated in the beginning with a tangible solution. User innovation furnish lead role to learners and connect students with educators, entrepreneurs, researchers and stakeholders such as their family members and other people of the community. They work collaboratively in a partnership to disseminate breakthrough product or a solution to a problem which connect students to the real world.
The following framework depicts how learner is transformed into a learning leader and his deeper learning skills are improved:
Where D1: collaboration
D2: communication
D3: learning mindset
D4: critical thinking
D5: learning how to learn
In the framework, learners’ deeper learning (D) skills (D1, D2 & D3) are improved when they act as partner and creator of learning along with teachers and other people of the community. Their D1, D3, D4 and D5 skills are improved during co-creation of product or solving any problem by a lead user method. Artificial intelligence or machine (deep) learning transforms D2 and D5 skills of learners whereas learning leadership improves their learning process.
How Deep Learning helps in improving metacognition and learning process of learners?
The activities performed generally in an online course do not provide enough insights as it gives simple information about abilities of students such as data gathered through multiple choice questions which furnish teachers with a limited information on students’ learning ability and do not inform them about their process of learning. Teachers need to understand how students think or reason and how they arrive at final decision to solve a problem? Data comes out of traditional way of teaching, learning and assessment is of limited value as it solicits final answer given by students instead of evaluating the intermediate process of their learning. The learning activity performed by students such as framing a problem, solving it, writing content online through lead user theory during an open essay question enhances collection of huge data. Therefore, taking learners as equivalent to partners along with teachers for creating content in an open answer question gives them full freedom and it furnishes enough data with significant insights on their learning process.
Deep learning can help in improving the learning process by building learning models which are data driven. Koedinger, Mclaughlin and Stamper (2014) have emphasized on building explanatory and actionable models rather than predictive ones to understand metacognitive awareness of students. Also, Koedinger et al. (2014) stated that cognitive task analysis (CTA) is efficient to analyze data gathered through technology. This data is of course created by learners and how they create is another woe because most of the knowledge learners possess is unconscious which they have gained from their past experience. A new medium is required which can aware them about their past learning experiences so that they could connect them with the new learning concepts. It means how to motivate students to create and how to help them to convert their tacit knowledge into explicit which is again a tricky task. What if we will get to know the existing cognitive structure of each learner in relation to the concept being studied and how they understand, process and organize information? If it happens, then it would be easy to understand tacit knowledge of learners and they can easily convert it into explicit information which will surely help them to document online. Mapping mind of students can help in understanding their cognitive structure, and cognitive mapping is utilized to understand conscious and unconscious aspects of learner’s mind. Cognitive mapping is an internal representation of the experienced world. According to David Ausubel, there is a semantic relation between concepts and relationship provides the basic structure for organizing knowledge. In a brief, we learn from experience and new concepts and problems have links with the older ones. These semantic relations or network links previous knowledge with the new one and help in understanding the cognitive structure of learners. The principle behind the semantic network is information which is saved in the long-term memory with regard to the categories or concepts linked with each other in terms of their meaning. Semantic network is a part of artificial intelligence or machine learning which can be created for a learner to understand one’s cognitive structure, interaction behavior, learning process and thus, improve their learning mindset. Semantic network can be used to understand how learners solve problem and to analyze their mental structure. Likewise, knowledge representation of experts such as teachers or people of community can help learners in comprehending experts’ cognitive structure which will assist them to relate new concept with the previous knowledge and thus experts’ tacit knowledge can be explicitly transferred to learners.
Knowledge creation is the best way to improve deeper learning skill of learners specifically if they create and innovate with full autonomy along with other associated people. A partnership with teachers and other experts during creating product or solving a problem improves their learning mindset and artificial intelligence helps in this process by mapping experts mind and transferring their knowledge to learners for helping them to understand tacit knowledge which experts possess. During traditional methods of teaching and learning, teachers used to transfer knowledge to students while students acted as a listener only. However, since the era of online learning, teachers or instructional designers are still transferring knowledge and students’ participation in the learning is still low, thus, they immerse in superficial learning than the deeper one. Knowledge transfer should be a knowledge creation and curation where students in equal partnership with teachers, entrepreneurs and stakeholders create and curate knowledge, solve problems, produce products and apply knowledge to the real world.
REFERENCES
1. American Institute for Research (September, 2014). Does deeper learning improves student outcomes? Retrieved from http://www.air.org/project/study-deeper-learning-opportunities-and-outcomes
2. American Institute for Research (August, 2016). Does deeper learning improves student outcomes? Retrieved from http://www.air.org/project/study-deeper-learning-opportunities-and-outcomes
3. Atchley, W. (2013). Comparison of course completion and student performance. files.eric.ed.gov/fulltext/EJ1017510.pdf
4. Atchley, W., Wingenbach, G., & Akers, C. (2013). Comparison of course completion and student performance through online and traditional courses. The International Review of Research in Open and Distributed Learning, 14 (4). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1461/2627
5. Croxton & Rebecca, A. (2014). The role of interactivity in student satisfaction and persistence in online learning. Journal of Online Learning and Teaching, 10 (2), p.314. Retrieved from http://connection.ebscohost.com/c/articles/97080957/role-interactivity-student-satisfaction-persistence-online-learning
6. Koedinger, K. R., Mclaughin, E. A., & Stamper, J. C. (2014). MOOCs and technology to advance learning and learning research: data-driven learner modeling to understand and improve online learning. Ubiquity, May, 1-13. Doi: 10.1145/2591682
