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1. Introduction

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)

3. Methodology

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

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%
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

Note: Table made from bar graph.

5. Conclusions

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.

Author Contributions

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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Author:Sion, Gratiela
Publication:Analysis and Metaphysics
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Date:Jan 1, 2018

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