=Paper=
{{Paper
|id=Vol-2630/paper_2
|storemode=property
|title=Changes in Knowledge Representation and Student-Learning Content Interaction in Digital Environment
|pdfUrl=https://ceur-ws.org/Vol-2630/paper_2.pdf
|volume=Vol-2630
|authors=Tatiana Noskova,Tatiana Pavlova
}}
==Changes in Knowledge Representation and Student-Learning Content Interaction in Digital Environment
==
https://ceur-ws.org/Vol-2630/paper_2.pdf
Changes in Knowledge Representation and
Student – learning Content Interaction
in Digital Environment∗
Tatiana Noskova Tatiana Pavlova
noskovatn@gmail.com pavtatbor@gmail.com
Herzen State Pedagogical University of Russia,
St. Petersburg, Russian Federation
Abstract
Learning activities in the modern digital environment reflect the diversity and functionality
of human-machine interaction and overlap innovative presenting knowledge models. Digital in-
telligent learning resources are focused on high levels of student cognitive activity and innovative
developmental potential. The new qualities of such learning activities are instrumentality, adapt-
ability, interacting with conceptualised and visualised knowledge.
Effective combinations of traditional and advanced presenting content methods in digital ed-
ucational resources make provision for training differentiation, student’s motivation support, and
addressing personal preferences. Shaping of demanded cognitive skills and digital competencies
become possible if the student is involved in the personal knowledge construction, aware of ad-
vanced learning goals, priorities, and capabilities of interaction with digital learning content.
Keywords: digitalisation of the learning environment, educational resources, learning content,
digital competence, learning content design
1 Introduction
Digitalisation of the educational environment leads to changes in the learning process. To organize
educational activities, an important role is played by digital educational resources operating in a
variety of information systems. Educational resources of the developing digital environment have
significant features: they reflect new trends of knowledge society, as well as promising educational
priorities related to the personalisation of the learning process and digital competencies shaping. This
generates a need for new approaches in digital learning content design. Due to various multimedia
technologies and intelligent algorithms automating information processes, key changes relate to ways
and structures of knowledge representation in human-machine systems. The problem contains not only
the insufficient distribution of “non-classical” educational content structures and promising interactive
technologies but also the insufficient educators’ and students’ readiness to interact effectively with
new knowledge structures using new digital tools.
∗
Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
1
2 Development
2.1 Changes in resource requirements in the digital learning environment
Any educational resource represents a certain content structure and provides definite possibilities
for interacting with learning content. In the first stages, the “digitization” of traditional educational
resources took place, with the preservation of “knowledge” paradigm capabilities. In digital imple-
mentations, they were enriched with interactive and multimedia functions that provide usability, but
do not change the way to acquire knowledge.
Promising society development trends, education should reflect, today are largely targeted to the
construction of a knowledge society and digital economy. Both of these trends are based on the rapid
progress of information technology, the exponential quantitative growth of available information and
the generation of new knowledge. In education, knowledge reproductive priorities are replaced by
goals related to the ability to acquire and operate knowledge personally and purposefully in solving
various problems.
The education and self-education today are not limited to a certain set of textbooks and teach-
ing aids. The UNESCO report “Towards knowledge societies” [Towards knowledge societies, 2005]
stresses the expanding of public knowledge space, associated primarily with the spread of ICT and
the Internet. The openness, richness, and connectivity of the knowledge space define new require-
ments for extracting knowledge human activity. Skills to search and identify information, its critical
assessment, analysis, processing and inclusion in the personal knowledge base are of great importance.
Since an information overload does not ensure an increase in personal knowledge, the digital resources
mission focuses on new knowledge organization to provide society demanded learning outcomes.
The modern knowledge space represents a network organization and opens up the possibility to
integrate educational, scientific, cultural, and informative content [Phillips et al., 2017]. The personal
nonlinear trajectories in complexly organized knowledge are impossible without analytical skills and
a critical content approach. Students don’t get such skills by default but gradually develop them
supported by the appropriate design of educational resources. Accordingly, in the learning process,
tasks should be set to encourage a person not only to consume knowledge but also to be actively
involved in the current knowledge processes in an information environment. Consequently, the learn-
ing content didactic methods are changing defining the new design of digital educational resources
[Xie et al., 2018].
An essential feature that distinguishes the student’s interaction with digital educational resources
is an instrumentality, i.e. the ability to carry out active actions with digital learning content using a
variety of digital tools. These actions support both the active student’s knowledge constructing and
shaping of new cognitive competencies.
Thus, the new requirements for digital educational environment resources correlate with the
knowledge coverage, knowledge organization (knowledge structures), and the instrumental interaction
with digital learning content.
2.2 Organization of knowledge and training activities in interpersonal educa-
tional interaction
New digital resources design approaches do not reject the reliable traditional didactic experience. This
experience is undergoing new understanding, enrichment, and transformation, taking into account the
new informational realities and the new demands for education.
The explanatory and illustrative teaching approach in classroom practices are based on con-
sistent, linear knowledge presentation, supplemented by visual and practical techniques. Students’
independent work, organized with textbooks, is also mainly based on the explanatory principle. In
traditional textbooks, the well-known methods for student’s orientation in a linear learning text,
visualization, attention, perception management, and activation (questions, tasks and etc.) are im-
2
Figure 1: New requirements for the resources of the digital educational environment
plemented. Student’s cognitive actions are mostly planned by the teacher: perception (listening,
reading), the predetermined cognitive procedures, summarising notes). The structure of knowledge
is mainly linear-thematic.
It can be concluded that the classical methods of transmitting social experience based on a
consistent, narrative, explanatory and illustrative way of presenting learning content, represent a pas-
sive form of the student-learning content interaction, i.e., the active person in presenting educational
information is the teacher.
2.3 Changes in interaction with knowledge in human-computer systems
The learning environment is changing, human-computer systems are playing an increasingly important
role. Student’s interactions with digital learning content are carried out mainly in an individual mode,
at the request of the user. Computer technologies expand the range of user’s actions with digital
learning content [Bando et al., 2017].
Direct transfer of traditional didactic approaches to the digital environment is not effective
enough as it does not take into account the potential of computer technologies for organizing knowl-
edge and ensuring the variability of learning behavior in an educational environment. Consequently,
it’s required to explore new ways of knowledge representation and student’s cognitive activities in
human-computer systems.
Addressing the priorities of individualised flexible knowledge management in an open informa-
tion environment, it is helpful to explore progressing instrumental opportunities for student’s active
interaction with learning resources, especially in terms of stimulating the various cognitive student’s
activity. V. Davydov asserted that not always the assimilation of knowledge and skills educates a
creatively thinking person, developing the human mind, and personality [Davydov, 1996].
Different types of digital learning content provide the learner with a specific possibility, due to
principles of structuring, granulating information, and the algorithmisation of human-content inter-
action.
Digital learning resources transformation stages. We distinguish three transformation stages
of digital learning resources design (concerning the classical book structures). Transformations are
3
carried out in various ways of knowledge formalization in computer systems, integrating presenting
content methods, and algorithmisation of learner’s interactions with digital content. By knowledge
formalisation, we mean the representation of knowledge using semiotic systems. Algorithmisation of
interaction with the digital learning content implies a arrangement of the student’s actions with the
digital content to achieve the result.
The closest to the classical book digital learning content organisation stage is notionally named
the level of “usability transformation”. Knowledge transferred to the digital environment with in-
significant transformations in structure, but with enrichment in usability (integration of information
sources, hypertext, multimedia representations of learning objects). The information granulation and
interaction algorithms are explanation logic and screen display subordinated. A significant advantage
is the student’s ability to navigate and control the digital content. The learner’s interaction method
is mainly aimed at perception, subjective reflection of educational information.
The second stage is conventionally denoted as the stage of the “open information base” in the
digital learning environment. It is characterised by a variety of information sources and knowledge
structures. Digital learning content is granular with multiple connections with the external informa-
tion environments and is open for active instrumental transformations by students using a variety of
digital tools (software). It expands the range of learning goals, in particular, in the frame of cognitive
activity. The student’s interaction with learning content terns more algorithmic. We mean the inter-
activity of student’s actions broadly, including in this concept both programmed human-computer di-
alogue, and various software tools applications providing flexible student’s abilities to manage learning
content. We denote this interaction method as “interactive instrumental” [Noskova & Pavlova, 2019].
The third stage of digital learning resources transformation provides automated learner’s interactions
with digital learning content. This is the stage of “intelligent digital resource base”. Modern intelli-
gent systems integrate knowledge storage and dissemination methods in natural and formal language,
text, graphic, audiovisual, multimedia modes. They allow the learner to involve extensive arrays of
educational, scientific, experimental information to receive automated feedback and analysis. Learner
avoid manual actions searching relevant and actual learning content. He solves learning tasks and
professionally significant problems on a high intelligent level. The student considered a multi-factor
cognitive system interacting with the complex, redundant information system [Xie et al., 2018][ et
al., 2019].
Information, extracted by the student from the knowledge base following personal requests and
preferences, can be used for solving problems with variable gains in performance. Instrumental solving
problem actions can be focused on the development of certain personal intellectual abilities. Such
digital educational resources design approach significantly changes the nature of learning activities
and we name it “intelligent transformation” [Noskova & Pavlova, 2019].
Let’s explore each of these stages in terms of knowledge structure and ways to interact with the
learning content of the digital educational environment.
Empowering classical knowledge structures in a digital environment. Currently the ma-
jority of digital learning resources are based on narrative-written social experience transferring (texts
of hypertext textbooks, video lectures, commented presentations, etc.). They replicate prior educa-
tional practices, but have undoubted advantages in terms of enrichment of visual aids, and interactive
functions. Digital multimedia resources integrate diverse content presentation modes. The educa-
tional context can be enriched by digital media publications, scientific information (online scientific
databases, Internet conferences, forums), artistic-figurative elements (theater, music, cinema, visual
arts, etc.), and professional communities’ discourse. The emotional-sensual sphere is more involved
in the cognitive process.
Interactivity is the leading distinguishing quality of digital multimedia resources. It activates
student’s interaction with the educational content, supports various types of learning actions: select-
ing actual content, observing static and dynamic objects, automated self-control, context-sensitive
4
Figure 2: New opportunities for digital learning content organizing and digital resource base trans-
formation stages
help, and so on. Hypertext represents the transition from linear sequences to “discretised” texts,
based on effective organisation of semantic autonomous content blocks, organized considering screen
information perceiving peculiarities. The optimal duration of a video lecture is determined by the
specifics of perception of discrete dynamic sequences.
Hypertextual knowledge structures are open (the content and structure of knowledge can be
changed and expanded). Applying a hypertext learning resource, the student’s activity can be aimed
both at studying the proposed knowledge sequences and at expanding digital learning content, sup-
plementing it with critically selected information. Thus, students acquire opportunities to extend
the learning context, to evolve aspiration to get the current state of knowledge and to utilize the
most reliable and relevant information (publications in authoritative scientific databases, professional
network communities, etc.). In consequence of automated text translation, the information in foreign
languages becomes available in the learning process.
But the hypertext learning content, enriched with multimedia and external information sources,
is not a sufficient condition for significant transformations of traditional student interaction with
knowledge. Learning activity is predominantly focused on the search, selection, study, assimilation
of structured content. The expansion of the possibilities of interaction with non-linearly structured
content is associated with free navigation, a detailed study of images and multimedia objects (video
clips, interactive animations, computer models), automated control and self-control. Such interaction
allows a person to shape individual routes in accordance with the accepted goals. Sufficient hypertext
branching and content saturation allow us to take into account students interests and preferences.
However, these routes are still largely subordinate to the logic and hierarchical structure of the
proposed content. The predominant student interaction with the digital learning resource is aimed at
knowledge acquisition and shaping primary skills for it’s application. But such learning resources not
much contribute to flexible personal learning and thinking skills that are of great importance with
respect to effective activity in the knowledge society.
Alternative representing knowledge structures in the digital environment. High levels
of learning content interactivity are associated with great extension of student’s actions with digital
components. At the same time, principles of knowledge discretisation are changing. New knowledge
5
structures gain ground, and accordingly appear new ways to organize learning and cognitive activities
and new possibilities to formulate learning tasks and solve learning problems.
The student’s interaction with digital learning content acquires a personalised activity, i.e.
learner activity is associated with the transformation of information to be assimilated. Digital learn-
ing content is intended to be personally changed, linked with other resources, and be expanded and
detailed in the process of solving problems. Digital tools make it possible to obtain new, personalised
information objects that demonstrate not only effective knowledge mastering, but also knowledge
deepening and expanding manifesting motivation, interdisciplinary links, and interpretation compe-
tencies.
Highly interactive information structures predominantly have a network organization, use au-
tomation tools for interaction with extensive information arrays. They function as an information
environment in which the student independently chooses action methods to achieve learning goals and
find a response to his preferences and motivations. Active scientific search and testing of intelligent
systems, virtual, hybrid, augmented reality, and semantic networks in education are in progress. But
the role and significance of such technologies is not essential in mass pedagogical practice.
Methods to formalize and organize knowledge are mainly based on the modern theory of large
systems or complex systems [Roth & Thom, 2010]. Differentiated formalising knowledge methods
include: natural language description (procedures and word processing operations), lexicographical
description (dictionary idea, encyclopedias), thesaurus description (logical and Intuitively, hypertext,
ontology, information retrieval qualifier), the formal language (logic models, calculation, algorithms).
Natural language descriptions are the most common in information sequences. But with an
exponential increase in information volumes and information technologies progress, they form the basis
for the functioning of intelligent automated systems for analyzing and processing text data, systems
with natural-language interfaces. The lexicographic knowledge description is highly formalised, while
the thesaurus knowledge description in information systems determines the knowledge flexibility as
openness, integration, multi-input, granularity, customisation.
Information technologies based on knowledge formal-language descriptions open up fundamen-
tally new automated functions by procedures determining basic elements, sets, syntax rules, axioms,
and knowledge extraction semantic rules. In modern intelligent systems, the most common ways of
representing knowledge are ontologies, semantic networks, frames, products [Gavrilova et al, 2016].
The ontological organization of knowledge is a finite set of concepts of a subject domain, united by
a variety of relations between concepts and many interpretation functions defined between concepts.
The structures of discrete knowledge are united by connections, which can be either unambiguous or
diverse, dynamic. Frames and frame networks serve operating knowledge representation units with
description details changing according to the current situation. That is of great importance for non-
permanent knowledge areas. Such methods of learning content representation in digital environment
allow a person to interact not only with large volumes of information and data but also to receive the
results of automated intelligent processed knowledge (based on formalisation, organisation and anal-
ysis methods). Intelligent knowledge extraction is applicable for solving complex learning problems
and research tasks.
We see that digital knowledge structures and knowledge formalisation methods indicated above,
are not intended for ready-made knowledge reproduction and assimilation. Intelligent knowledge
bases, although they require new skills, support new cognitive activities not based on sequential
knowledge perception, requiring divergent thinking in various situations and interpretations, that is
important for advanced professional competencies. For effective students’ knowledge management,
conceptual learning object links and relationships are of essential importance. Complex ideas and
problems are usually difficult to convey in linear text. The knowledge structure visualisation opens up
new ways to reveal the causes and goals of knowledge relationships in the studied problems context.
6
2.4 The critical role of knowledge representation and conceptualisation models
in shaping students’ learning, thinking skills, and digital competencies
The main problem of modern teaching practice is the method to organize active personal learning and
cognitive activities based on various information structures implemented in a digital environment.
This activity should be flexible both concerning students’ information preferences and knowledge
management in the process of solving learning tasks. In a personalized learning process, the student
not only accepts the goals of solving educational problems but also individually defines and transforms
them following personal meanings, capabilities, preferences. He determines the ways to achieve an
educational result, plans his actions, selects resources and suitable tools, runs communication.
To demonstrate the multidimensional role of advanced knowledge representation and concep-
tualisation in digital learning resources we apply the Guildford’s model. Guildford identified three
basic intelligence factor groups as a result of classification per three autonomous variables in infor-
mation processing. These variables are: the information content, intelligent information process-
ing operations, and information processing results. Content variables presuppose images, symbols
or formal signs, semantics (conceptual information), and behavior (information reflecting motives,
needs, moods, thoughts, attitudes, etc.). Information processing variables cover cognition (detection,
recognition, awareness, understanding of information), memory, ability to convergent and divergent
thinking, ability to evaluate information. The information processing results comprise items of infor-
mation, allocation of information classes (ability to classify), setting unit relations, objects relations,
systematization, designing integral content networks, conversion, modification, reformulation, as well
as implication (conclusions beyond the framework of the available information) [Edwards, 1969].
The flexibility and functionality of information structures and content models (figurative, sym-
bolic, semantic, behavioral) in digital learning resources promotes setting personal learning objectives
and personalised learning activities. Traditional sequential knowledge mastering is inefficient if learn-
ing content presented as a hypermedia semantic network. Flexible information structures serve to
set multilevel learning tasks: for analysis, defining classes, relationships, systems. The productive
knowledge application in the learning process contributes to creative problems solving in new and
nontypical situations. It is essential to provide a variety of intelligent student’s actions applying dig-
ital learning tools. D.P. Guildford identifies operations affecting shaping cognitive functions, mem-
ory, divergent and convergent thinking, ability to evaluate. In particular, the convergent thinking
evolvement associated with information actions focused on comparison, generalization, classification,
categorisation, abstraction, analysis of information and knowledge, as well as the synthesis of specific
information structures and objects [Guildford, 1965]. The divergent thinking criteria involve a com-
plex of integrity and systematicity, criticality, flexibility, reflectivity in assessment, productivity, and
the ability to generate innovative ideas [Dryazgunov, 2003]. The main advantages of the network,
ontological knowledge representation and the conceptual knowledge visualisation are versatility and
suitability. Consequently, such digital resources provide a flexible basis for learning in an evolutive
context. M.A. Holodnaya associates this kind of learning to going beyond the system, finding or
creating a new product, searching for new solutions [Holodnaya & Gel’fman, 2016].
In this respect, learning tasks promoting productive students activity, utilising ontologies or
semantic networks representing subject area or problem domain, can give a significant contribution
to students’ competencies shaping. Tasks focused on deeper learning, interpretation, and heuristic
applying knowledge in various contexts, present the learning outcomes shift from knowledge assimi-
lation to the constructing personal knowledge structures, and ability to apply knowledge to complex
questions. Such opportunities and learning goals are adequate to the students’ competencies highly
demanded in progressing digital economy and knowledge society. Advanced formalisation and con-
ceptualisation knowledge methods in digital learning resources should not be fragmentary. Students’
appropriate skills and competencies acquisition requires a systematic transformation of the digital
learning environment.
7
Moreover, digital competencies comprise not only the ability to apply automated knowledge
interaction software tools and information systems, but also an understanding of their operation
principles, and the knowledge structures. The innovativeness of human interaction with knowledge
in a digital environment should be perceived. Applying future-oriented knowledge models and gain-
ing new experience, students come closer to modern intelligent technologies comprehension such as
machine translation and text analysers, semantic search, and recommender systems. New opera-
tional framework of learning activities is straightly associated with new knowledge representation
and conceptualisation models. Conditions ensuring effective interaction with knowledge in a digital
environment is a component of high-quality training.
2.5 Tools for productive interaction with knowledge in a digital environment
One of the meaningful educational goals is the active student position shaping concerning the available
information resources. This position implies the perception of educational, cultural, professional
sources of information, not only as a means for specific learning tasks but also as personal progress
means that ensures success and competitiveness in the modern society. Human interaction with a rich
and heterogeneous information environment is facilitated by digital tools for searching, extracting,
automated translation, processing and presenting in various formats information and knowledge.
Digital tools practices expand the range of educational goals: enhance students’ ability to acquire
knowledge personally, support various attitudes, and own position working out pragmatic, research,
and professional tasks.
Accordingly, digital learning resources are not a passive information component of a digital ed-
ucational environment. They considered as the information basis of active and productive learning
activities, involving knowledge from a variety of sources, encouraged by digital tools with high in-
formation action freedom, for example, modeling tools, semantic textual models, conceptual visual
models, computer simulations. Semantic textual models and user-driven ontology mapping tools sup-
port collaborative activities in finding correspondences between concepts in two or more knowledge
areas. Mind mapping and organizational chart tools allow student to structure knowledge, highlight
supporting concepts, model the relationships, and contribute to the intuitive misconceptions identi-
fying and the creative ideas generation. The objectives of an active, productive instrumental student
interaction with the redundant and diverse resources of a modern digital educational environment
is the suitable knowledge discovery, its inclusion in personal knowledge set, and new significant and
situationally relevant knowledge synthesis. Such objectives are adequate to the potential of the digital
educational environment in which the student, being active, not only consumes available resources
but also designs personal digital information space.
Conclusion
Learning activities in the modern digital environment overlap the potential of resources providing
traditional knowledge structures and innovative formalising and presenting knowledge. Traditional
knowledge structures in digital mode obtain significant advantages in terms of content integration,
“usability”, and customisation. The variety of productive interaction digital tools enable developing
learning tasks based on digital information structures openness (the possibility of learning content
restructuring, transcoding, expansion, etc.).
Digital resources implementations in intelligent systems concepts have innovative developmental
potential as they are focused on high levels of student’s cognitive activity and motivation contexts.
Digital knowledge formalisation, network, ontological knowledge structures, and visual conceptuali-
sation support fundamentally new learning practices. The new qualities of such learning activities
are adaptability, the ability to operate with significant information collections, interacting with self-
learning systems, and apply formalized knowledge to solve poorly formalized tasks.
8
Situations of an effective combination of traditional and advanced presenting and organising
content methods in digital educational resources are determined by many factors, as knowledge area
specificity, training level, learning tasks, student motivation, and personal preferences. Demanded
cognitive skills and digital competencies shaping becomes possible if the student is involved in the
personal knowledge construction, aware of advanced learning goals, priorities, and capabilities of
instrumental interaction with educational resources.
With all the diversity and functionality of modern human-machine interaction, not all classes
of learning problems can be solved, applying even sufficiently flexible and adaptive knowledge repre-
sentation methods. First of all, this refers to tasks associated with students’ values and personality
cultivation. Traditional narrative technologies, technologies based on interpersonal interaction, are
still more effective.
Acknowledgement
The research was supported by RSF (project No 19-18-00108).
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