Influence of the Digital Technologies to the Process of Learning

MICHAEL GR. VOSKOGLOU

Mathematical Sciences, School of Technological Applications

University of Peloponnese (ex T.E.I. of Western Greece)

Meg. Alexandrou 1, 26334 Patras

GREECE

Abstract: - The present paper studies the influence of the digital technologies to the process of learning. A

common principle of all the traditional learning theories, which developed in a time when learning was not

taking place through technology, is that learning occurs inside a person. In today’s digital environment,

however, we frequently need to act by drawing information which is stored within a database or an organization

and is manipulated by technology. The traditional learning theories do not address this kind of learning, defined

as actionable knowledge and occurring outside of people. The need to bridge this gap led to the development of

connectivism, a new theory for understanding learning in our digital era. The paper outlines the headlines of

connectivism, which is based on an integration of principles related to chaos, networks, and self-organization

theories, and exposes briefly the reported criticisms for it and the recently developed teaching approaches

related to it. A framework is also presented, due to Siemens, for organizing and comparing the primary

traditional learning theories with connectivism. Our final conclusion is that none of the existing theories can

stand alone as a complete theory for learning. The combination of them, however, seems to provide an adequate

framework for understanding the process of learning.

Key-Words: traditional learning theories, teaching methods, artificial learning, digital technologies,

connectivism.

Received: July 5, 2021. Revised: February 19, 2022. Accepted: March 22, 2022. Published: April 19, 2022.

1 Introduction

Learning, a universal process that all individuals

experience, is a fundamental component of human

cognition. It combines cognitive, emotional and

environmental influences for acquiring or enhancing

one’s knowledge or skills.

Curiosity about how humans learn dates back to

the ancient Greek philosophers Socrates, Plato and

Aristotle, who explored whether knowledge and

truth mostly come from intellectual reasoning, i.e.

they could be found within oneself (rationalism) or

through external observation (empiricism).

Thousands of years later, during the 17th and 18th

century, the same question was the reason for a

historical confrontation of two academic schools of

European philosophy: The rationalists Descartes,

Spinoza, Leibniz, versus the U.K. empirists Bacon,

Locke, Hume.

By the 19th century, psychologists began to

answer this question with systematic scientific

studies. Volumes of research have been written

about learning and many theories have been

developed for the description of its mechanisms.

The goal was to understand objectively how people

learn and then develop teaching approaches

accordingly.

The third Industrial Revolution (IR), however,

which started in the 1940s and is widely referred as

the era of automation [1], has transformed, with the

help of computers and other “clever” machines of

Artificial Intelligence (AI), the human society to the

digital world of our days, where technology is

present in almost every aspect of our lives. Further,

a fourth IR started in the beginning of the 21st

century [2, 3], characterized, among others, with an

advanced Internet of Things (IoT), which will

provide energy, goods and services at the right time

and at any place. There is no doubt, therefore, that

our students should take full advantage of the

potential that the new digital technologies can bring

for improving their learning skills.

The present work discusses the influence of the

new technologies to the process of learning resulted

to connectivism, a new learning theory for the

contemporary digital human society. The rest of the

paper is formulated as follows: In section 2 the

WSEAS TRANSACTIONS on ADVANCES in ENGINEERING EDUCATION

DOI: 10.37394/232010.2022.19.8

Michael Gr. Voskoglou

E-ISSN: 2224-3410

Volume 19, 2022

traditional learning theories and the corresponding

teaching methods developed during the last two

centuries are briefly exposed. In section 3 the

extensive use of computers and methods of AI in

Education during the last years as well as the

benefits and limitations of the artificial with respect

the traditional teaching and learning methods are

summarized. The headlines of connectivism, the

criticisms about it and the recently developed

teaching approaches related to it are exposed in

section 4. In section 5 a framework is presented, due

to Siemens, for organizing and comparing the

primary traditional learning theories with

connectivism. The article closes with section 6

containing our final conclusion and some hints for

future research.

2 Traditional Learning Theories and

Teaching Methods

During the 20th century, the debate among the

learning specialists centred on whether people learn

by responding to external stimuli (behaviorism) or

by using their brains to construct knowledge from

external data (cognitivism).

Behaviorism, a theory established by the

American psychologist John B. Watson (1878–

1958), considers learning as the acquisition of new

behavior based on environmental conditions and

discounts any independent activities of the mind

asserting that we do not know what occurs inside

the learner (a “black box” activity) [4].

Cognitivism, which replaced behaviorism during

the 1960′s as the dominant theory for the process of

learning, argues that knowledge can be seen as a

process of symbolic mental constructions and that

learning is defined as change in individual’s

cognitive structures [5]. More explicitly, the

learning process involves representation of the

stimulus input, i.e., use of the contents of one’s

memory to find the suitable input information,

interpretation of the input data to produce the new

knowledge, generalization of this knowledge to a

variety of situations and categorization of it in the

already existing learner’s cognitive schemata. In this

way the individual becomes able to retrieve, when

necessary, the new information from his/her proper

cognitive schema and to use it for solving related

problems. Changes in the learner’s behavior are in

fact observed, but only as an indication of what is

occurring in his/her mind. In other words, cognitive

theories look beyond behavior to explain the brain-

based process of learning.

Constructivism, a philosophical framework based

on Piaget’s theory for learning and formally

introduced by von Clasersfeld during the 1970s,

suggests that knowledge is not passively received

from the environment, but is actively constructed by

the learner through a process of adaptation based on

and constantly modified by the learner’s experience

of the world [6]. This framework is usually referred

as cognitive constructivism.

The synthesis of the ideas of constructivism with

Vygosky’s social development theory [7] created

the issue of social constructivism [8]. According to

Vygosky, learning takes place within some socio-

cultural setting. Shared meanings are formed

through negotiation in the learning environment,

leading to the development of common knowledge.

The Communities of Practice (CoPs), for instance,

are groups of people, experts or practitioners in a

particular field, with a concern for something they

do and they learn how to do it better as they interact

regularly, having therefore the opportunity to

develop personally and professionally [9]. The basic

difference between cognitive and social

constructivism is that the former argues that

thinking precedes language, whereas the latter

supports the exactly inverse approach.

In addition to the primary learning theories, i.e.

behaviorism, cognitivism and constructivism,

several other options about the nature of learning

have also appeared [10]. Humanism, for example,

focuses on creating an environment leading to self-

actualization, where learners are free to determine

their own goals while the teacher assists in meeting

those goals. The experiential theory suggests to

combine both learning about something and

experiencing it, so that learners be able to apply the

new knowledge to real-world situations. Also, the

transformative theory, which is particularly relevant

to adult learners, considers that the new information

can change our world views when paired with

critical reflection, etc.

The role of teaching is to promote the learning of

the corresponding subject. Some decades ago, the

dominant teaching method used to be the explicit

instruction (EI), which is mainly based on principles

of cognitivism. The teacher is in the “center” of this

method and tries with clear statements and

explanations of the teaching context and by

supported practice to transfer the new knowledge to

students in the best possible way [11]. The main

criticism against EI is that it may prevent conceptual

understanding and critical analysis [12]. Many

educators, therefore, adopting ideas of

constructivism, enriched the EI with a series of

challenging questions so that to keep an active

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Michael Gr. Voskoglou

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discourse with students, as a means to promote

critical thinking [13].

Constructivism and the socio-cultural theories for

learning have become, however, very popular

during the last decades as a basis for teaching,

especially among teachers of the elementary and

secondary education. New teaching approaches have

been introduced, like the problem-based learning,

the inquiry-based learning through creative

exploration, the formation of virtual CoPs among

students and teachers, etc.

A typical teaching method developed across

these lines is the “5 E’s” instructional treatment

[14]. The acronym “5 E’s” is due to the five

successive phases of that treatment including

engagement, exploration, explanation, elaboration

and evaluation. The “5E’s” method promotes the

fruitful interaction among students and teachers and

facilitates the production of the new knowledge on

the basis of prior knowledge and experiences.

3 Computers and Artificial

Intelligence in Education

In this section we discuss briefly the extensive use

of computers and methods of AI in Education

during the last years, as well as the benefits and

limitations of the artificial with respect the

traditional teaching and learning methods.

Computers provide through the Internet a wealth

of information to teachers and learners, while suitably

referred as Smart Learning Systems (SLS’s), give to

the instructor the opportunity to apply innovative

teaching and learning methods in the class, like the

APOS/ACE instruction, the flipped learning, etc., that

increase the student imagination and problem solving

skills [1, 15-22].

The ontologies in computer science are

knowledge-based intelligent systems designed to

share knowledge among computers or among

computers and people. Apart from helping the

instructor in the search of learning materials and

pedagogical resources in the internet, ontologies are

also useful for the evaluation of the students’

learning performance and for recommendations and

grouping of them based on their learning behavior

and skills [23-24].

An effort started during the 1980’s to re-create

the individual tutoring in a computer (adaptive

learning systems). AI focuses in general on

developing personalized curricula based on each

student’s specific needs. A grand experiment is in

progress in China that could change the way that

people learn. Squirrel and Alo7 are two of the first

China’s companies to pursue the concept of an AI

individual tutor [25].

E-learning gives to the learner 365 days per year

access to the learning subject in contrast to the

traditional learning, which is scheduled as a one-time

class and requires the learner’s physical presence.

Another advantage of e- learning is that it can be

used at the same time by a large population spread

throughout the world. The e-learning material, once

developed as a course, could be easily modified in

future for similar uses. Through e-learning students

can learn in their own speed what is important for

them by skipping unnecessary information. In

addition, e-learning is obviously much cheaper than

the traditional one, which involves many extra costs

(travel, boarding, books, etc.) [26]. In concluding, e-

learning appears today as a promising alternative to

traditional classroom instruction, especially in cases

of remote lifelong learning and training, while it can

also be used as a complement of the classroom

learning.

When engaged in the Case-Based Reasoning

(CBR) approach with many past cases available,

students become able to recognize more alternatives

and to benefit from the failures of the others. Cases

indexed by experts will reveal to students suitable

ways of looking at a problem, a thing that they may

not have the expertise to do without the help of a

CBR system. The CBR methodology is useful in

particular for situations where there is much to

remember, because when reasoning analogically one

tends to focus only on the few possible analogous

past cases [27].

A social robot is an AI machine that has been

designed to interact with humans and other robots.

Social robots have been already used for entire job

functions at home by understanding speech and

facial expressions, in customer service, in education,

etc. Two important examples for education are the

robot Tico that has been designed to improve

children’s motivation in the classroom and the robot

Bandit that has been developed to teach social

behavior to autistic children [28, 29].

The impressive advances of AI in the field of

Education outlined above have made a number of

specialists on the subject to be certain that in future

computers and the other “clever” machines of AI

will replace teachers in educating students.

However, although literature experiments have

demonstrated that in certain cases artificial learning

(i.e. learning acquired by using methods and

techniques of AI) can be at least as effective as the

conventional classroom learning, we are not in a

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E-ISSN: 2224-3410

Volume 19, 2022

position to claim that it can replace the traditional

classroom instruction in general [26].

In fact, in contrast to the above-mentioned

advantages, there are also certain limitations of the

artificial with respect to the traditional learning. One

of them is that in the distance learning the queries of

a student cannot be solved instantly, as the physical

presence of the teacher in the classroom guarantees.

Also, students in the classroom are pushed through

the course to learn, whereas not every student finds

e-learning suitable for his or her style. For example,

some students feel bored in front of a computer.

Therefore, although today thousands of online

courses are offered by universities all around the

world, many of them leading to degree or certificate

awards, several uncertain issues and technical

problems have to be further investigated concerning

the effectiveness and status of artificial learning.

4 Connectivism: A New Learning

Theory

The traditional learning theories outlined in section

2 were developed in a time when learning was not

taking place through technology. A common

principle of these theories is that learning occurs

inside a person. In today’s digital environment,

however, we frequently need to act by drawing

information which is stored within a database or an

organization and is manipulated by technology. The

traditional learning theories do not address this kind

of learning, defined as actionable knowledge and

occurring outside of people.

4.1 The Headlines of Connectivism

The need to bridge this gap led to the

development of the idea of connectivism, which

appears as a new theoretical framework to

understand learning in the digital age. Connectivism

was first introduced in 2004 by George Siemens on

a blog post which was published as an article in

2005 [30] and it was expanded by a publication of

Stephen Downes’ [31]. Both works received

significant attention and an extended discourse has

followed since then on the appropriateness of

connectivism as a learning theory and its

technological implications. In 2008, Siemens and

Downes delivered an online course called

"Connectivism and Connective Knowledge" [32]. It

covered connectivism as content while attempting to

implement some of their ideas. The course was free

to anyone who wished to participate, and over 2000

people worldwide enrolled. This reveals the interest

of people for the new theory for learning in the

digital age. Following the central presentations, the

attenders could participate with their choice of tools

to express their own views and remarks. The model

of this course, which was repeated in 2009 and in

2011, was successfully characterized by D. Cormier

and B. Alexander by the term “Massive Open

Online Course” (MOOC).

Connectivism presents a model of learning that

acknowledges the current shifts in society where

learning is no longer an internal activity of the

individual. At its core, is a form of experiential

learning which prioritizes actions and experience

over the idea that knowledge is propositional. Its

central idea is that our ability to learn what we need

for tomorrow is more important than what we know

today. Consequently, when knowledge is needed,

but not known, the ability to plug into sources to

meet the requirements becomes a necessary skill.

Learning is focused on connecting specialized

information sets, and the connections that enable us

to extend our knowledge are more important than

our current state of knowing. The theory of

connectivism is based on an integration of principles

referred to networks, to the science of chaos and the

self-organization theory.

A network can be defined as a system of

connections between nodes, which is based on the

principle that its nodes can be connected to create an

integrated whole. Node is understood to be anything

that can be connected to another node, such as an

organization, a database, images, feelings, etc.

Connectivism sees knowledge as a network and

learning as a process of creating new connections

and expanding the network’s complexity.

Chaos recognizes the connection of everything to

everything. It is well known, for example, the half-

jokingly remark that a butterfly stirring the air today

in Peking could transform storm systems next

month in New York [33, p.8]. In contrast to

constructivism, which states that learners attempt to

foster understanding by meaning-making tasks,

chaos states that the meaning exists and the learner's

challenge is to recognize the patterns which appear

to be hidden.

Self-organization is defined as the spontaneous

formation of well-organized structures, patterns, or

behaviors, from random initial conditions [34, p.3].

Learning as a self-organizing process requires that

the learning system (personal or organizational) can

change its structure in order to be able to classify its

own interaction with an environment.

4.2 Criticisms

The idea of connectivism as a new theory for

learning has drawn various criticisms. The most

important of them are the following:

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Michael Gr. Voskoglou

E-ISSN: 2224-3410

Volume 19, 2022

Verhagen [35] speaks for the ineffectiveness of a

theory based on “unsubstantiated philosophizing”

and considers connectivism as a rather pedagogical

view.

Kerr [36] claims that although technology affects

learning environments, existing learning theories are

sufficient.

Kop and Hill [37] conclude that while it does not

seem that connectivism is a separate learning theory,

it "continues to play an important role in the

development and emergence of new pedagogies,

where control is shifting from the tutor to an

increasingly more autonomous learner".

Ally [38] recognizes that the world has changed

and becomes more networked, so learning theories

developed prior to these global changes are less

relevant. However, he argues that what is needed is

not a new stand-alone theory for the digital age, but

a model that integrates the different theories to

guide the design of online learning materials.

Chatti [39] notes that connectivism misses some

concepts which are crucial for learning, such as

reflection, learning from failures, error detection and

correction, and inquiry.

Al Dahdouh [40] examined the relation between

connectivism and Artificial Neural Network (ANN)

and the results, unexpectedly, revealed that ANN

researchers use constructivism principles to teach

ANN with labeled training data, whereas

connectivism principles are used to teach ANN only

when the knowledge is unknown.

4.3 New Teaching Approaches

As the popularity of using technological tools

grows, the autonomy of learners and their control

over access to information is continuously

increasing. Several educators developed models of

teacher and learner roles and interaction for our

digital era.

Seely Brown [41], describing learning as an

“enculturation practice”, compares the class with an

atelier and presents the teacher as a master artist

who observes the student activities and draws

attention to innovative approaches.

In Fisher’s [42] model the teacher is compared

with a network administrator whose main role is to

assist learners in forming connections and creating

learning networks.

Bonk [43] presents teacher as a concierge

directing students to resources or learning

opportunities that they may not be aware. The

concierge provides a form of “soft” guidance, either

incorporating traditional lectures or permitting

students to explore on their own.

Siemens [44] compares teacher with a curator,

who instead of dispensing knowledge, creates

spaces in which knowledge can be explored,

constructed and connected. He also notes that

instructional designers, due to the developing

complexity of tools and availability of open

education resources, play an educational role of

directing educators to tools and resources

5 Organization of the Learning

Theories

Ertmer and Newby raised five questions on the

purpose of distinguishing the learning theories [45].

Siemens [44], by answering these questions for each

theory provided a framework for organizing and

comparing the three primary traditional learning

theories outlined in section 2 (behaviorism,

cognitivism, constructivism) together with

connectivism. Ertmer’s and Newby’s questions and

the Siemens’ answers for the four theories in the

series that they have been previously mentioned are

the following:

1. How does learning occur?

- Through observable behavior (what occurs

inside the learner is a “black box activity”)

- Structured, computational, through mind

activities

- Social, meaning created by each learner

(personal) as a result of social influences.

- Could be outside the learner distributed

within a network, social, technologically

enhanced, recognizing and interpreting

patterns

2. What factors influence learning?

- Nature of reward, punishment, stimuli

- Existing schema, previous experiences

- Engagement, participation, social, cultural

- Diversity of network, strength of ties, digital

technologies.

3. What is the role of memory?

- The hardwiring of repeated experiences,

where reward and punishment are most

influential

- Encoding, storage, retrieval

- Prior knowledge remixed to current context

- Adaptive patterns, representative of current

state, existing in networks

4. How does transfer occur?

- Stimulus, response

- Duplicating knowledge constructs of the

learner

- Socialization, constructing the new with the

help of the previous knowledge

- Connecting to (adding) nodes

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DOI: 10.37394/232010.2022.19.8

Michael Gr. Voskoglou

E-ISSN: 2224-3410

Volume 19, 2022

5. What types of learning are best explained

by this theory?

- Task‐based learning

- Reasoning, clear objectives, problem

solving

- Social, vague (ill defined)

- Complex learning, rapid changing core,

diverse knowledge sources

This framework enables the user to obtain his

(her) own conclusions about the philosophy, the

advantages and disadvantages of each of the

examined learning theories.

6 Conclusion

The continuously increasing use of the new

technologies in Education has changed significantly

the landscape around learning, which nowadays can

take place outside the individual in the form of

actionable knowledge.

From the discussion performed in this paper, our

conclusion is that none of the existing theories can

stand alone as a complete theory for learning. In

fact, behaviorism attempts to determine and

understand learning with respect to its outer

indications on the individual’s behavior, cognitivism

focuses on the study of the internal mechanisms of

the human mind for acquiring learning and

constructivism turns the attention to the suitable

ways for conquering learning. All these theories do

not address the learning taking place outside people

in our digital era and connectivism attempts to

bridge this gap. Our belief, however, is that the

combination of all these theories provides an

adequate framework to study and understand the

process of learning. In particular, and despites the

various criticisms that have been drawn,

connectivism seems to stand satisfactorily as a

complement of the traditional learning theories for

the digital era. At any case, further research is

needed for the correlations and ties of connectivism

with the traditional learning theories, as well as for

the new roles that teachers and learners are expected

to play in our digital era.

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WSEAS TRANSACTIONS on ADVANCES in ENGINEERING EDUCATION

DOI: 10.37394/232010.2022.19.8

Michael Gr. Voskoglou

E-ISSN: 2224-3410

Volume 19, 2022