Review of Cognitive Energy Flow Model Concept for Virtual Student

Viktors Zagorskis

and Atis Kapenieks

Distance Education Study Centre, Riga Technical University, Kronvalda 1, Riga, Latvia

Keywords:

Cognitive Energy, E-leaning Quality, Computer Agent, Virtual Student.

Abstract:

Data analysis in Virtual Learning Environment deepens the understanding of cognition processes in real stu-

dent’s brain. The challenge is the evaluation of the quality of e-learning courses before the large-scale im-

plementation. With this aim, we formulate the concept for a computer model for Virtual Student’s evolution.

We combine knowledge elements explored from learners behavior data and cognitive theories. We assume

that some of the brains energy ﬂow expenses in learning and memorization are due to energy extraction for

applying existing skills, analysis of accumulated knowledge, and adaptation of newly available information.

We argue that the proposed Virtual Student model can perform cognitive energy ﬂow modeling by extracting

energy from the environmental learning objects and losing the power in a tedious learning process. The re-

search shows that Cognitive Energy Flow model can be computerized to produce synthetic data to improve

e-learning courses and predict real student’s behavior.

1 INTRODUCTION

Intelligent Agents introduced a breakthrough in com-

puter science because of autonomy, ubiquity, human

centric orientation and, what is essential, human-like

behavior. Among the number of different agents cat-

egories, Learning Agents are more advanced because

they have the human-similar ability to learn from ex-

perienced interaction with the host learning system.

Since 1950th, intelligent agents used to use almost

in all of the computer systems with the aim to get

the system objects information, to rethink and pro-

vide local feedback or public actions. Usually, intel-

ligent agents follow some road-map: (1) Perception,

(2) Cognition, and (3) Actuation. These are functional

properties of almost all the independent autonomous

agents.

In the current research, we propose to invent

new features shrinking the gap between improved

intelligent agent namely Virtual Student (VS) and real

on-line learner behavioral properties. We reﬂect the

concept of improved Learning Agent Model with the

following additional properties: (1) emotional states,

(2) ability to forget the learned facts, (3) need for rest,

and (4) agent’s energy ﬂow. Concerning that, we pro-

pose the improved learning agent model based on the

agent’s energy ﬂow modeling is the crucial point of

https://orcid.org/0000-0002-6155-0570

our interest in a proposal of a new Learning

Agent model.

We aim to create the preliminary model as a con-

cept that would further help to computerize and sim-

plify real learners’ behavior classiﬁcation problems.

We assume to apply traditional predictive analytics

methods on outcomes of Virtual Student operation

where either (1) usage of Machine Learning Methods

are not cost-efﬁcient or (2) the environment has new

- not approved learning courses and learners have not

produced any data yet.

Such, the Virtual Student’s cognitive energy ﬂow

model-based approach improves computerized agent

model to be applicable for Predictive Analytics (PA)

methods applied to real learners without uneconom-

ical Machine Learning (ML) operations. This state-

ment is the current research question.

The research organized as follows: Section 2 -

the reﬂexion of related theories, methods, and ap-

proaches. In Section 3, that is the most important

in the research we propose the concept of the Virtual

Student’s Cognitive Energy Flow Model. In Section

4 we provide a discussion of results and conclude the

paper.

542

Zagorskis, V. and Kapenieks, A.

Review of Cognitive Energy Flow Model Concept for Virtual Student.

DOI: 10.5220/0007768205420549

In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 542-549

ISBN: 978-989-758-367-4

2 RELATED WORK

The large-scale picture on human-like Cognition ap-

pliances in a Computer-intelligence success like Ar-

tiﬁcial Intelligence (AI), Machine Learning (ML), or

particularly in VLEs has the roots based on some his-

torical conclusions expressed in learning theories.

2.1 Theories of Cognitive Development

Undoubtedly, any learning process aims to gain

knowledge results. Mainly, the learning process is

cognitive because the learner develops itself regard-

less of supervised or unsupervised learning style.

The most inﬂuential theories of Cognitive Develop-

ment categorized as: (1) Piaget’s theory, (2) So-

ciocultural theories, (3) Core-knowledge theories,

and (4) Information-processing theories. The last

- Information-processing theories are well adaptable

and known in a computer applications domain.

About 1920, Piaget developed the ﬁrst ”Cognitive

theory” mapped on child cognitive development. At

that time, Piaget’s the revolutionary idea was to con-

sider a child as a scientist providing experiments on

their (Piaget and Campbell, 1976).

Three of Piaget’s main essential processing cate-

gories (Piaget, 2005) are: (1) construct own learner

knowledge from experimenting on the world, (2)

learn many things on their own without the interven-

tion of helpers, and (3) to stay intrinsically motivated

to learn and do not require rewards to incentivize

learning.

Among the criticisms of Piaget’s Theory the most

strong are: (1) learners’ thinking process is affected

by social interactions (Vygotsky’s Theory), (2) young

children have and use much inborn mental machinery

for complex abstract thought (Core-Knowledge The-

ories), (3) thinking is not as consistent as the theory

suggests, and (4) thinking is a computational process.

The last two criticisms belong to authors of Contem-

porary Theories. The most relevant example adopted

from contemporary theories is the Information Pro-

cessing approach considering child development as

a computer model development. Therefore, we con-

clude that modern methods of Cognitive Development

are pointing again back to the discussions about the

Child cognition.

The full theory of intelligence is not yet in ex-

istence, although we assume that Master Theory of

Cognitive Development should exist in the future

(Domingos, 2015), and is applicable in a Human or

Artiﬁcial World.

2.2 Sense, Perception, Cognition, and

Semantics

Overall, Artiﬁcial Intelligence (AI) held on a cou-

ple of key concepts: Sensing, Perception, Cognition,

and Semantic (Sheth et al., 2015). Such processes

came into AI systems view to delegate computers to

solve human-speciﬁc sensing, perception, and think-

ing problems. Also, we have to consider that a real

person does not perceive the real world, the person

ﬁrst interprets what he/she sees and then simulate the

next event in their mind. The magic is still, how we

learn and get the subconscious to do things effort-

lessly.

Assuming that Computer-Agent (as a computer

program) have Senses to receive various data from the

digital learning environment host, the next conscious

process in a computational sequence is the Percep-

tion: a cyclic process of interpreting data. Percep-

tion involves both interpretation and exploration with

a ﬁrm reliance on background knowledge patterns of

the domain of application (Gregory, 1997).

Cognitive computing follows the Perception and

aims to develop a coherent, uniﬁed, and universal

mechanism for understanding problems around the

Computer-Agent. Cognition utilizes all the data re-

ceived from a Perception act. Similarly like in a per-

ception process, cognitive computing context is pro-

vided by the existing knowledge base (Modha et al.,

2011). Overall, the cognition is the process of gaining

knowledge.

Finally, Semantics layer involves mapping obser-

vations from various stimuli on Computer-Agent in-

put, such as tactile, speech, or visual signals, to con-

cepts and relationships as humans would interpret and

communicate them. Semantics stays out of current re-

search scope.

2.3 Cognitive Learning and Cognitive

Engineering

Cognitive Engineering is a method of study using

cognitive psychology to design and develop engineer-

ing systems aimed to support the cognitive learning

processes of users (learners). A person starts with

goals and intentions that are psychological variables.

Although, Psychological Variables differ from Phys-

ical Variables used in engineering systems (Norman,

1987).

In the case of real learner modeling, the computer

agent must interpret the learning object physical vari-

ables into terms relevant to the psychological goals

and must translate the mental intentions into physi-

cal actions upon the application algorithms. Overall,

Review of Cognitive Energy Flow Model Concept for Virtual Student

543

the Cognition Engineering challenge is to study and

design the process of acquiring knowledge through

Agent’s thoughts, experiences, and senses. Cogni-

tive Learning involves obtaining knowledge through

experience, study, and being taught by Computer-

Agents (Brown and Fehige, 2017). At the same time

Computer- Agents can be used as Agents that learn.

Learning and Cognition are two almost identical

concepts, although cannot occur without each other:

Learning requires Cognition, and Cognition involves

Learning.

Cognitive Process. Of full value, cognitive pro-

cess implementation serves goals to complete match-

ing micro-architectures: Senses (Intensity of Sensa-

tion), Affection (Weber’s Law - quantifying the per-

ception of change in a given stimulus), Attention

(Rate, Duration, Degree, Inertia), Perception (Tempo-

ral, Qualitative, Quantitative(Simple, Complex)), As-

sociation (Law of Association), Memory & Imagina-

tion (Cache Operative(a couple of seconds), Middle,

Long Term Storage Network), and Action (Emotions

& Thoughts). Individual cognitive system require-

ments can cause simpliﬁcation of the whole architec-

ture or more detailed research and design of the spe-

ciﬁc item.

Levels of Cognitive Learning. An ordering of cogni-

tive skills usually are arranged based on Bloom’s tax-

onomy (ﬁrst edition 1956) contemporary transformed

into a new version. The revisited version (Anderson

et al., 2001) includes six levels: (1) Remembering,

(2) Understanding, (3) Applying, (4) Analyzing, (5)

Evaluating, and (6) Creating. Although, we ﬁnd an

applicable reduced version of taxonomy: (1) Memo-

rization, (2) Understanding, (3) Application.

Despite the reduction, the minimized approach in-

cludes all the skills above the Application level as the

ability to apply the knowledge. For the proposed Vir-

tual Student model, we ﬁnd three knowledge levels

acceptable.

Cognitive Cycles. Neuroscientists have indepen-

dently proposed ideas similar to the cognitive cycle:

cascading cycles of recurring brain events. (Fuster,

2002; Baars and Franklin, 2007). Notably, that re-

search results in psychology (Franklin and Graesser,

1997; Anderson et al., 2004) show that cognition

in autonomous agents, whether artiﬁcial, animal or

human, can be thought of as consisting of repeated

perception-understanding-action cycles.

Cognitive Cycles Timing. Results from studies in

neurosciences determine the length of time taken by

each of the phases of the cognitive cycle also, are well

known. Some results successfully adopted for spe-

ciﬁc architectures, for instance: LIDA (Learning In-

telligent Distribution Agent) (Madl et al., 2011). We

also integrate Cognitive Cycles Timing results to later

proposed Virtual Student’s model.

2.4 Thoughts on Energy in Learning

Process

2.4.1 Mental Energy

The idea of energy associated with mental activity

dates far back in human History and cultures. For-

mulation of Mental Energy (a hard mental effort) in

scientiﬁc magazines belong to Julian Huxley (Hux-

ley, 1944). The problem complexity relies on the ori-

gins of Human Mental Energy correlation to physical,

social, and mental health impacting the real learning

process. It is a generally recognized truth physical

events considering in two ways: from the mechanistic

and from the energic standpoint (JUNG, 1969).

2.4.2 Time and Energy

The fundamental principle of causality and a propor-

tional (linearity is not the obligation) connection be-

tween time and energy is acceptable for later use to

build the Virtual Student Energy Flow model.

2.5 Intelligent Agents

A very simple agent model (Eq. 1) is an abstract con-

cept that can be deﬁned mathematically as an agent

function:

f : P

∗

decision

−−−−→ A (1)

If P

∗

is a data set of perception on input of model,

and A is deﬁned as action on model output, then f is

by function deﬁned simple agent.

In general, an agent receives information through

its sensors. Decision making is an internal functional

facility of Intelligent Agent Model. Such a simpliﬁed

deﬁnition gives insight into concerns about the possi-

ble complexity of concise and valuable intelligence of

models.

Overall, exist more than one Intelligent Agents

classiﬁcation schemes proposed by Russel (Russell

and Norvig, 2003) and Weiss (Weiss, 2013). We fol-

low Russel classiﬁcation, where group agents divided

into ﬁve classes based on their degree of perception

and capability. Russel evolves ﬁve agent groups: (1)

simple reﬂex agents,(2) model-based reﬂex agents,

(3) goal-based agents, (4) utility-based agents, and

(5) learning agents. Any other proposed models have

variations based on classical concepts of agents ar-

chitecture, their ability to perceive, control the action

reasoning, and to act on sensing networks.

CSEDU 2019 - 11th International Conference on Computer Supported Education

544

Intelligent Agents can act either as a single in-

stance or in groups: multi-agent systems. In the cur-

rent paper, we discuss a single instance model.

3 DISCUSSION AND OUTCOMES

The current research question is: The cognitive en-

ergy ﬂow model-based approach shows another way

to evaluate empty learning course in a digital learning

environment where learners have not produced any

data yet.

The new approach goal is to create the preliminary

model that would help simplify real learners’ behav-

ior forecast and classiﬁcation problems using tradi-

tional predictive analytical methods in a digital learn-

ing environment.

Based on the essential concepts revealed in the

previous section, we propose a new learning agent

model named Virtual Student The new model takes

into account such quite complicated to explore com-

mon human emotional conditions like relaxation,

boredom, excitement, and anxiety to explore human

emotional conditions.

We specify some crucial principles we follow cre-

ating Virtual Student’s model: (1) reliable Mental En-

ergy Flow Model for Virtual Student is the primary

interest of our research, (2) Virtual Student’s Learn-

ing Process is a Mental Energy Flow expressed as a

consummation of Internal Energy or gain from inher-

ited Learning Objects, (3) every single mental activity

is a transition along the learning path rewarded with

a speciﬁed but ﬁnite Energy Portion - Energy Token

if the change has a direction to the comfortable emo-

tional condition, (4) if the transition has a direction to

the uncomfortable emotional condition, Virtual Stu-

dent becomes ﬁned by Energy Token Decreasing, (5)

Virtual Student initially has enough Energy Tokens to

overcome threshold level to join the Learning Course,

or to start to explore speciﬁc Learning Object, (6) Vir-

tual Student runs based on the principle of causality

and a proportional (linearity is not the obligation) re-

lation between time and energy.

On the research roadmap, ﬁrstly we draft the

Learning Energy ﬂow boundaries for Virtual Stu-

dent’s evolution model. Then, we specify Virtual Stu-

dent’s properties. Finally, we propose the Virtual Stu-

dent Learning Model concept ready for operational

implementation.

3.1 Virtual Student Learning Energy

Centered Ecosystem

3.1.1 Learning Energy Network

Here, we invent the isolated learning network with

boundaries for Virtual Student operations when sens-

ing, and declare rules for reasoning (perception, cog-

nition) and acting. Learning network bounds energy

consumption or production. In other words - the

amount of internal energy U in the ecosystem is con-

stant. Equation (2) formalize this assumption. Each

component in the ecosystem holds their energy de-

noted as E

. Under such a restriction the only way to

keep U constant and simultaneously provide energy

ﬂow is the process of energy redistribution among

system components. We follow the simple princi-

ple: no energy - no action. Therefore, to operate,

the model system energy should be greater than zero:

U > 0.

U =

∑

= Const. (2)

We specify three top class energy-related objects

for the ecosystem and their properties:

• System Energy Depot - E

• Virtual Student’s Energy Buffer - E

V S

• Learning Object Energy Storage - E

In the model, learning network Enthalpy we specify

as:

U =

∑

V S

∑

(3)

3.1.2 Initial Energy Flow Considerations

Invented Initial Energy Depot holds a certain amount

of energy distributed among other components on

system simulation start. For our experiment, we use

one System Energy Depot, one Virtual Student’s in-

stance, and a speciﬁed number of Learning Object

with various but speciﬁed learning related energy

value. As follows, for one Virtual Student Equation

(3) transforms into:

U = E

+ E

V S

∑

(4)

Equation (4) depicted in Fig. 1 applying di-

rections of possible energy ﬂow. Firstly, at the

learning process initialization, each Learning Object

(LO

. . . LO

) receives speciﬁc initial energy portion

. . . E

). Next, Virtual Student’s Energy Buffer

(VS) receives their initial amount of energy sufﬁcient

Review of Cognitive Energy Flow Model Concept for Virtual Student

545

Figure 1: Energy Flow Model for Virtual Student’s Ecosys-

tem: System Energy Depot (Depot), Virtual Student’s En-

ergy Buffer (VS), and k Energy Storages for Learning Ob-

jects (LO

. . . LO

to start the simulation based on some algorithmic con-

siderations.

3.1.3 Energy Flow Control Algorithm

After the Virtual Student’s simulation start, their in-

stance sequentially interacts with Learning Objects

imitating all the phases of the cognition process. In

the case of success, indicated as the corresponding

ﬂag in the algorithm, Virtual Student receives energy

portion from the Learning Object it communicated.

Learning fortune path transfers energy tokens as re-

wards from Learning Objects to Virtual Student. A

decision regarding learning fortune rewards amount

depends on the Virtual Student: (1) Virtual Student’s

self-assessment score, (2) Virtual Student’s assess-

ment by Learning Object requirements gathered from

submission, (3) Virtual Student’s emotional condi-

tions after the task ﬁnishing, and (4) on time spent

for learning. Learning fortune path is active only at

favorable emotional conditions like excitement or re-

laxation. Similarly, indications of negative emotions

(anxiety, boredom) lead to energy tokens loss from

Virtual Student’s Energy Buffer. As mentioned be-

fore, the ecosystem model assures energy is return-

ing to system depot. Also, we argue that Virtual Stu-

dent’s energy reduction is a consequence of effort in

a conventional learning process. Thinking on ecosys-

tem model parameters, we consider that real average

learner can hold approximately two or three learning

tasks in their attention at the same time, or one com-

plex task. Task complexity metrics in the ecosystem

the point of interest. Here, we identify learning ob-

jects as tasks.

3.1.4 Initial Energy Flow Conditions

In the beginning, sufﬁcient initial energy amount E

V S

can be assigned to Virtual Student. If energy assigned

to Learning Object E

> E

V S

, we say that this is

border condition not to start a learning process di-

rected to speciﬁc Learning Object. If all the Learning

Objects in a system have energy level bigger than spe-

ciﬁc Virtual Student’s energy, learning cannot start at

all. Such simple rules allow modeling various

initial learners’ conditions.

3.1.5 Optimal Energy Flow Conditions

Energy awareness is a central interest point of energy

ﬂow control algorithm. That is to say, a Virtual Stu-

dent with a capability of estimating its energy ﬂow

in the cognitive learning process can determine the

potential points for energy optimization. Such an ap-

proach requires both (1) sensing data and (2) compu-

tational schemes based on the learning model.

3.1.6 Final Energy Flow Conditions

During the model runtime, energy control algorithm

follows the energy balance principle. As we stated

above, ﬁnal conditions lead to process termination

based on energy comparing rules.

3.2 Virtual Student Properties

We invent the following groups of static and dynamic

properties that characterize Virtual Student: (1) need

the rest property, (2) ability to forget the learned facts,

(3) emotional states, and (4) cycling through motiva-

tional sequences. The cycling through motivational

sequences depending on real student’s emotions is

crucial for the Virtual Student model proposal and

later discussed in details.

Need for the Rest property involves relatively

slow action modeling daily workout process. Just as

the human need rest and recuperation the Virtual Stu-

dent must re-stock on vital energy from system depot.

Such an approach allows simulating long-term inac-

tivity gaps like housekeeping, sleeping, and vacations.

”Need the Rest” property simulation results lead to

the opportunity to study memorization and forgetting

depending on Virtual Student’s idleness and leisure.

We suggest that current property activation would im-

prove the Virtual Student’s conformity to a real one.

Albeit forgetting is a human-like property, we use

Forgetting property to add a new research vector.

Forgetting curve, adopted from Murre follows the Ex-

ponential Distribution, whereas Memorization mod-

eled as the Poisson Arrival Process (Murre et al.,

2013) and is useful for Virtual Student’s memory

model. For Virtual Student model, we apply three-tier

memory architecture: sensory, short-term, and long-

term memory. We also accept The 24-hour point of

upward jump in Ebbinghaus’ forgetting curve (Murre

and Dros, 2015).

Also, in the further development of memory pro-

cess control algorithm, we propose to include the

module for memorization processes’ volume control

CSEDU 2019 - 11th International Conference on Computer Supported Education

546

- an option for the Virtual Student to practice the

memory and become smarter. For further references

let’s name the memorization processes’ volume con-

trol module as MVC.

Emotional States. With this property, we under-

stand real learners’ emotional conditions playing a

speciﬁc role in the learning modeling conﬁdence.

In the next section, we discuss, specify, and utilize

four emotional categories classiﬁed as follows: very

pessimistic, skeptical, conﬁdent, and very conﬁdent.

Considering correlation to Virtual Student’s energy

model, we elaborated the Emotional States factors.

Positive emotions incentivize learners subconsciously

growing their energy, although negative sentiment -

lead to stuck, depression, energy loss to learn.

Motivational Sequences. To form Virtual Student

emotionally motivated interactions with ecosystem

layers and components required for cognitive learning

process modeling, we adopt a commonly occurring

Apter’s Motivational Sequence model (Apter, 1989).

Fig. 2 depicts adopted Motivational Sequence model

mapped to the timeline. Virtual Student’s attempts to

catch and hold certain pleasant states like excitement

or relaxation are alternating with unpleasant ones like

ﬂat, tiresome states.

Figure 2: Van der Molen Motivational Cycling model trans-

formed into Motivational Sequence model by mapping to

timeline.

In the amended Motivational Sequences model

(Fig. 2), we deﬁne the excitement state as the ini-

tial one, therefore indicating Virtual Student’s addic-

tion to learning. We argue that a tendency to begin

to learn at the emotional excitement phase correlates

with the real learners’ motivation keen to learn. By

standing anxiety as the next position in the sequence,

we realize that in reality, human behavior follows the

same path: positive emotions and negative emotions

are in the constant cycling process. Therefore, we

simplify the Motivational Sequences model avoiding

the direct transition to any other state except catego-

rized as emotionally opposite.

Four alternating model states represent cycling

through comfort levels: excitement, anxiety, relax-

ation, boredom mapped to the timeline. The comfort

cycling model mapping to energy-based model fol-

lows a simple idea: if the emotional state classiﬁed as

negative, Virtual Student does not receive the energy

and starts to lose one proportional to the time spent

in such a condition. By replacing a single emotional

Comfort Level vector (Fig. 2) with two Learning En-

ergy vectors beginning at the point of ”Passivity” we

invent the Energy Fluctuation model (Fig. 3). The far-

ther from middle (”Passivity”) level resides the emo-

tional state, the more energy we associate with such a

condition.

Figure 3: Four alternating model states represent emotional

cycling: a) adopted to timeline model, b) proposed Energy

Fluctuation model as a function of emotional cycling in

time.

In the ﬁgure, the shaded area considered as low

energy region or ”No Flow!” zone. Such an area

indicates Virtual Student being close to shut-off or

dropout in reality.

Alternating Emotional states represent emotional

cycling process having an impact on Energy Fluctua-

tion in the Virtual Student Learning Model. Follow-

ing diagram (Fig. 4 ) depicts one of the possible sce-

narios of possible strategic trends in Virtual Student’s

Energy Fluctuation model. Learning Energy gaining

in the next simulation step denoted as a ”FUTURE.”

Also, we invent the ”Comfort Zone” bounding Energy

Flow gulf for Virtual Student.

Overall, comparing of classiﬁed emotional states

is the beginning point to start to compare simulations’

outcomes and real learners’ classiﬁcation results. A

computerized model can apply such consequences in

a blended learning process for comparing real and vir-

tual students operating in one shared virtual learning

environment.

3.3 Virtual Student Learning Model

From Franklin’s results (Franklin and Graesser,

1997), we take into consideration the existence of uni-

versal cognitive cycling paradigm: cognition in au-

tonomous agents is subject independent - whether ar-

tiﬁcial, animal or human. This cycling paradigm is

the crucial concept point to follow.

At ﬁrst, we ﬁnd that Stringer’s Action Spiral

model (Nasrollahi, 2015; Stringer, 2013) stated the

Review of Cognitive Energy Flow Model Concept for Virtual Student

547

Figure 4: Energy Fluctuation model. Scenario: Suspicious

Energy boosting in both directions leaving a Comfort Zone

moving towards extreme threshold levels: hidden subburn-

ing (too much boredom) and overexpectations (too much

excitement).

existence of look-think-act cycles in cognitive learn-

ing correlate with similar results proposed by An-

derson (Anderson et al., 2004). Next, we conclude

that Anderson uses nouns: perception-understanding-

action, although Stringer applies verbs: look-think-

act to describe the same paradigm.

In our conceptual Virtual Student model, we apply

both emotions related and look-think-act cycles (Fig.

5).

Figure 5: Virtual Student Learning model Components:

subplot a) - Motivational Cycling (MC) model, and subplot

b) - Stringer’s Action Spiral (SAS) model.

Finally, by adding operation control logic, we

combine both motivational cycling and cognition cy-

cles approaches into one coherent system present-

ing the concept of Virtual Student Learning Model

(Fig.6).

For simplicity, the Virtual Learning Environment

on the proposed model depicted as a single simple

Learning Object (LO). The bi-directed line repre-

sents Virtual Student’s communication with LO. Vir-

tual Student’s requests for available data and retrieves

the next information portion proportional to the at-

tention quantity. Computerized VS model measures

such an effort and the time applied to the Learning

Object. The Learning Object’s response comes along

with LO’s speciﬁc METADATA set.

We suggest a real e-learning environment (VLE)

modiﬁcation by implementing VS related META-

DATA in speciﬁc LO’s model. A METADATA should

include at least ENERGY-Speciﬁc credentials. We

propose to apply to use Energy Tokens. Also, META-

DATA represents a set of speciﬁc Learning Object pa-

rameters like size, complexity, expected learning ef-

fort, average forgetting parameters, and virtual learn-

ing path constraints to control the cognitive process.

Figure 6: Interaction of Virtual Student Learning model

Components with Learning Object in a Spatial Design.

Also, Fig. 6 depicts the Virtual Student Learning

model in dynamics. Each transition on the scheme

denoted as a colored circle object with a sequence

number inside. Blue colored transitions, and corre-

sponding solid directed arcs reveal a path for the ﬁrst

stage of learning process switching from positive ex-

citement state to harmful one - anxiety. The route

goes via cognitive learning cycling (transitions 2, 3,

4, 4’). Transition 5 means a decision at a ”think”

state to get stuck. Next, after some arbitrary time

spent in state ”anxiety,” follows transition 6 return-

ing Virtual Student to a comfortable emotional level

- ”relaxation.” Green circles and dotted lines specify

the third phase in motivational sequences model lead-

ing to switching to the next uncomfortable - bore-

dom state. Finally, without discussions of reasons,

Virtual Student returns to the ”excited” state. Either

route goes through a decision-making component im-

plemented in the look-act-think module.

The main system algorithm controls the learning

process interacting with every module with the aim to

supervise ecosystem energy ﬂow. On a condition of

insufﬁcient enough energy, what is the worst scenario,

Virtual Student is dropped out of the course. In the

case of acceptable quality of interaction with learning

objects, the mission completed.

CSEDU 2019 - 11th International Conference on Computer Supported Education

548

4 CONCLUSION AND FUTURE

DIRECTIONS

Summarizing research results regarding energy as-

pects of the discussed model, we conclude: (1) Learn-

ing Energy redistribution ﬂow among the system ob-

jects can be observed and controlled by the main

system algorithm, (2) Learning Energy Ecosystem

model’s Energy Quantity is constant for every sim-

ulation run, (3) proposed Learning Energy Ecosystem

for Virtual Student evolution has clear operating con-

ditions to simulate the learning process based on the

energy balance principles, (4) proposed Virtual Stu-

dent will produce more synthetic data ready for vali-

dation of correlation with real user behavior data.

For future works, we consider the following con-

cept point: cognition for every autonomous agent is

subject independent. To approve such a concept we

consider: (1) tudy Virtual Student model computer

implementation depending on model Veriﬁcation re-

sults on the model validation stages, (2) translate the

conceptual model to operational one and verify it by

implementation into real Virtual Learning Environ-

ment, (3) build the computerized model, (4) apply

the proposed model in a blended learning process for

comparing both real and virtual students operating in

one shared virtual learning environment.

Further research by applying validation to the pro-

posed model with an implementation in the Virtual

Learning Environment might clarify the aspect of Vir-

tual Student’s potential.

ACKNOWLEDGMENT

This research has been supported by a grant

from the European Regional Development Fund

(ERDF/ERAF) project ”Technology Enhanced

Learning E-ecosystem with Stochastic Interdepen-

dences - TELECI”, Project No.1.1.1.1/16/A/154.

REFERENCES

Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S.,

Lebiere, C., and Qin, Y. (2004). An integrated theory

of the mind. Psychological review, 111(4):1036.

Anderson, L., Krathwohl, D., and Bloom, B. (2001). A tax-

onomy for learning, teaching, and assessing: a revi-

sion of Bloom’s taxonomy of educational objectives.

Longman.

Apter, M. J. (1989). Reversal theory: A new approach to

motivation, emotion and personality. Anuario de Psi-

colog

ıa, 42(3):29.

Baars, B. J. and Franklin, S. (2007). 2007 special issue:

An architectural model of conscious and unconscious

brain functions: Global workspace theory and ida.

Neural Netw., 20(9):955–961.

Brown, J. R. and Fehige, Y. (2017). Thought experiments.

In Zalta, E. N., editor, The Stanford Encyclopedia

of Philosophy. Metaphysics Research Lab, Stanford

University, summer 2017 edition.

Domingos, P. (2015). The Master Algorithm: How the

Quest for the Ultimate Learning Machine Will Remake

Our World. Basic Books.

Franklin, S. and Graesser, A. (1997). Is it an agent, or just

a program?: A taxonomy for autonomous agents. In

Proceedings of the Workshop on Intelligent Agents III,

Agent Theories, Architectures, and Languages, ECAI

’96, pages 21–35, London, UK, UK. Springer-Verlag.

Fuster, J. M. (2002). Physiology of executive functions: The

perception-action cycle., page 96–108. New York:

Oxford University Press.

Gregory, R. L. (1997). Knowledge in perception and illu-

sion. Philosophical Transactions of the Royal Society

of London B: Biological Sciences, 352(1358):1121–

1127.

Huxley, J. (1944). On living in a revolution, by Julian Hux-

ley. Harper New York, [1st ed.] edition.

JUNG, C. G. (1969). Collected Works of C.G. Jung, Volume

8: Structure & Dynamics of the Psyche, pages 3–66.

Princeton University Press.

Madl, T., Baars, B., and Franklin, S. (2011). The timing of

the cognitive cycle. 6:e14803.

Modha, D. S., Ananthanarayanan, R., Esser, S. K., Ndi-

rango, A., Sherbondy, A. J., and Singh, R. (2011).

Cognitive computing. Commun. ACM, 54(8):62–71.

Murre, J., Chessa, A., and Meeter, M. (2013). A mathe-

matical model of forgetting and amnesia. Frontiers in

Psychology, 4:76.

Murre, J. M. J. and Dros, J. (2015). Replication and analysis

of ebbinghaus’ forgetting curve. PLOS ONE, 10(7):1–

23.

Nasrollahi, M. A. (2015). A closer look at using stringer’s

action research model in improving students’.

Norman, D. A. (1987). Interfacing thought: Cognitive as-

pects of human-computer interaction. chapter Cog-

nitive Engineering&Mdash;Cognitive Science, pages

325–336. MIT Press, Cambridge, MA, USA.

Piaget, J. (2005). The Psychology Of Intelligence. Taylor &

Francis.

Piaget, J. and Campbell, S. (1976). Piaget Sampler: An

Introduction to Jean Piaget Through His Own Words.

Wiley.

Russell, S. J. and Norvig, P. (2003). Artiﬁcial Intelligence:

A Modern Approach. Pearson Education, 2 edition.

Sheth, A. P., Anantharam, P., and Henson, C. A. (2015).

Semantic, cognitive, and perceptual computing: Ad-

vances toward computing for human experience.

CoRR, abs/1510.05963.

Stringer, E. (2013). Action Research. SAGE Publications.

Weiss, G. (2013). Multiagent Systems. The MIT Press.

Review of Cognitive Energy Flow Model Concept for Virtual Student

549