SMART EDUCATIONAL ECOSYSTEMS: COGNITIVE ENGAGEMENT AND MACHINE INTELLIGENCE ALGORITHMS IN TECHNOLOGY-SUPPORTED LEARNING ENVIRONMENTS.
Far-reaching attempts to adopt higher-order judgment have entailed carrying out educational design involvements (Donnelly, 2017; Lazaroiu, 2017a; Meschitti and Smith, 2017; Stroe, 2018a) that embark learners on intricate cognitive undertakings. (Lee and Choi, 2017) Technology has considerably expanded the setting for learning with factual issues. Knowledge is more thoroughly designed via synergy with issue-oriented, socially established contexts. (Wang, Derry, and Ge, 2017)
2. Literature Review
In the technology-enhanced educational setting, technological affordances are employed to further learners' higher-order judgment undertakings. For such design elements to operate as planned, learner aspects such as epistemological principles, positions toward technology utilization, and instructional strategies should pave the way for the design intervention. (Lee and Choi, 2017) The ascendancy of machine learning at intelligence assignments is chiefly ascribable to its capacity to identify intricate structure not established beforehand (Friedman and Friedman, 2018; Lazaroiu, 2017b; Nica and Taylor, 2017; Stroe, 2018b): it fits elaborate and adjustable functional types to the information without just overfitting and detects functions that operate satisfactorily out-of-sample. (Mullainathan and Spiess, 2017) Deep learning is typified by a significant degree of commitment in education, galvanized by inherent motivation and reinforced by important instructional proposals that enable learners to handle complexity and essential difficult tasks to back involvement and attain a superior level of comprehension and accomplishment. (Wang, Derry, and Ge, 2017)
Using data from Deloitte, Forrester, HubSpot, Ironpaper, Narrative Science, O*NET database, and Statista, I performed analyses and made estimates regarding barriers to automation (social intelligence, creative intelligence, and perception and manipulation correlate with lower average automation index), percentage interested in interacting with artificial intelligence-based tools, use of artificial intelligence technology in enterprises (voice recognition and response solutions/machine learning/virtual personal assistants/systems used for decision support/automated written reporting and/or communications/analytics-focused applications/robotics), which machine learning use cases respondents have in place or planned in their enterprise, and what best describes the respondents' feeling of preparedness to bring humans (change management) and technology (operations, applications, and support) together to solve issues arising from smart machine technologies.
4. Results and Discussion
Problem-oriented education may assist learners in advancing critical judgment and problem-solving abilities in addition to strengthening and increasing subject-matter knowledge. (Wang, Derry, and Ge, 2017) In machine learning, subsequent findings should be inferred as precisely as possible, thus reducing prediction error. The relative carrying out of various types of machine learning algorithms may offer relevant insights into the character of the information. (Yarkoni and Westfall, 2017) While supplying a robust, adjustable manner of making first-rate predictions, lacking substantial and chiefly implausible inferences, machine learning does not bring about constant estimates of the intrinsic parameters. (Mullainathan and Spiess, 2017) (Figures 1-5)
Figure 2 Percentage interested in interacting with artificial intelligence-based tools I'm very I'd try it once I'd try it and interested out of curiosity if it works well I'll keep using it Overall 26% 32% 30% US 36% 27% 27% Latin America 34% 25% 37% UK and Ireland 21% 33% 28% Germany 15% 39% 22% I'm not interested Overall 12% US 10% Latin America 4% UK and Ireland 17% Germany 24% Sources: HubSpot; my survey among 3,400 individuals conducted May 2018. Note: Table made from bar graph. Figure 3 Use of artificial intelligence technology in enterprises Voice recognition and 36% response solutions Machine learning 27% Virtual personal assistants 18% Systems used for 11% decision support Automated written reporting 8% and/or communications Analytics-focused 8% applications Robotics 6% All of the above 11% Sources: Narrative Science; my survey among 2,800 individuals conducted June 2018. Note: Table made from bar graph. Figure 4 Which machine learning use cases respondents have in place or planned in their enterprise Asset management 47.6% Data discovery 44.9% Decision making 38.5% Risk management 37.9% Cybersecurity 35.5% Campaign and 31.2% sales program optimization Credit risk scoring 29.4% Pricing optimization 27.2% Cross channel analytics 26.8% High speed arbitrage trading 26.3% Forecasting and optimization 26.2% Ad targeting/selection 26.1% Network performance optimization 25.7% Campaign management and optimization 25.4% Market and consumer segmentation 25.1% Fraud detection/prevention 24.8% Customer satisfaction 23.6% Call detail record (CDR) analysis 21.8% Product recommendations 21.7% Clickstream segmentation and analysis 21.5% Energy network management/optimization 20.8% Abnormal trading analysis/detection 20.4% Event/Behavior-based targeting 20.2% Customer churn management 19.5% Market basket analysis 18.8% Events/Activity behavior segmentation 18.2% Supply chain analytics 17.9% Power generation management 17.2% Patient care quality and program analysis 15.1% Threat detection 13.2% Social graph analytics 13.1% Drug discovery and development analysis 12.8% Sources: Ironpaper; Statista; my survey among 2,400 individuals conducted June 2018. Note: Table made from bar graph. Figure 5 What best describes the respondents' feeling of preparedness to bring humans (change management) and technology (operations, applications, and support) together to solve issues arising from smart machine technologies Prepared (we have a defined set of 11% principles and best practices for both human and technology support) Partially prepared (technology operations, 35% applications, and support are understood, but human and organizational aspects are not) Partially prepared (human and organizational 16% aspects are understood, but technology operations, applications, and support are not) Somewhat prepared (internal teams 27% have been organized to study human and technology issues) Poorly prepared (we have limited 28% understanding of the human and technology implications) Sources: Forrester; my survey among 1,800 individuals conducted May 2018. Note: Table made from bar graph.
Learners' higher-order judgment is robustly and directly shaped by deep learning strategies, but not by epistemological positions toward technology utilization that (Alpopi and Silvestru (Bere), 2016; Lazaroiu, 2017c; Madudova, 2017; Popescu Ljungholm, 2017) indirectly alter higher-order judgment, facilitated via the learner's deep learning strategy. (Lee and Choi, 2017) Technology-supported educational settings are instrumental in furnishing adjustable access to data and schooling resources, on-demand transfer of instructional programs, manageable communication and social cooperation, adequate information processing, multimedia images, and computer-based learning reinforcement. (Wang, Derry, and Ge, 2017) Supervised machine learning algorithms explore operations that infer satisfactorily out-of-sample. Machine learning can handle atypical information that is too high-dimensional for established assessment methods (Gilbert, 2017; Leskaj, 2017; Peters, 2017) and is instrumental in preprocessing and imputing in conventional datasets. (Mullainathan and Spiess, 2017)
This paper was supported by Grant GE-1364728 from the Center for Labor Research and Education at AAER, Chicago, IL.
The author confirms being the sole contributor of this work and approved it for publication.
Conflict of Interest Statement
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Spiru Haret University, Bucharest
How to cite: Sion, Gra.iela (2018). "Smart Educational Ecosystems: Cognitive Engagement and Machine Intelligence Algorithms in Technology-Supported Learning Environments," Analysis and Metaphysics 17: 140-145.
Received 16 July 2018 * Received in revised form 12 September 2018
Accepted 22 September 2018 * Available online 9 December 2018
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|Publication:||Analysis and Metaphysics|
|Date:||Jan 1, 2018|
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