Adaptive and Blended Learning for Electrical Operators Training

With Virtual Reality Systems

Yasmín Hernández and Miguel Pérez Ramírez

Gerencia de Tecnologías de la Información, Instituto de Investigaciones Eléctricas, Reforma 113, Cuernavaca, Mexico

Keywords: Empathic Agents, Bayesian Networks, Blended Learning, Student Model, Virtual Reality.

Abstract: Due to the danger involved in the electrical field, qualified electricians are required. Traditionally, training

has been based on classroom courses and camp training, but it is costly and students need to spend a long time

to develop their competences. We propose to complement traditional training with an intelligent training

system composing a blended training model. The blended model enables adaptive training through a student

model which represents the affective and knowledge states of the trainees. The affect is recognized taking

into account a theoretical model of emotions. The knowledge of the student is updated as he interacts with the

system. The instruction is presented in a virtual reality environment by an empathic agent. The virtual reality

system enables practicing in a controlled and safe environment. In this paper, the general proposal for the

blended training model is presented.

1 INTRODUCTION

The electrical domain requires efficient and well

trained electricians because a manoeuvre badly

performed can result on accidents that can injure

people or damage costly equipment. However,

training personnel confronts problematic situations

such as the limited availability of electrical

installations that trainees need for practicing in real

environments, therefore the trainees have to help as

assistant of electricians for a long time, and due to

danger included, they first only observe the

manoeuvre. This limited opportunity to practice in

real environments makes training to take a long time

besides being costly.

In order to advance in the solution of this problem,

we are working in composing a blended learning

model. A definition states that blended learning is

learning that is facilitated by the effective

combination of different modes of delivery, models

of teaching and styles of learning, and founded on

transparent communication amongst all parties

involved with a course (Heinze and Procter, 2004).

In our proposal, trainees still attend classroom

courses but they complement learning and practice

aided by an intelligent training system. The intelligent

training system integrated virtual reality systems

which allow having a virtual representation of the

electrical environment. The virtual reality is the

electronic representation (partial or complete) of a

real or fictitious environment. Such representation

can include 3D graphics and/or images, has the

property of being interactive and might or might not

be immersive (Pérez and Ontiveros, 2009).

Another component is a trainee model

representing the knowledge and affect states of the

trainee. The trainee model enables adaptive training

as in an intelligent tutoring system (Woolf, 2009).

The trainee model is represented by Bayesian

networks. On the knowledge side the model includes

the topics the trainee has already learnt and the topics

the trainee does not know yet. On the affect side the

model includes what the trainee feels according to the

OCC model (Ortony, Clore and Collins, 1988) and to

the basic emotions proposed by Ekman and Friesen

(1978).

The blended learning model also includes an

empathic agent to be the face of the intelligent

training system.

In this way, we have a blended learning model

which enables adaptive and intelligent training where

the individual state of trainees is considered. The

training scenarios are presented as virtual

environments enabling valuable practice before going

to real electrical installations, and the learning is

facilitate by an animated agent.

Regarding the integration of the technological

pair: virtual reality and intelligent tutoring systems, it

Hernández, Y. and Ramírez, M.

Adaptive and Blended Learning for Electrical Operators Training - With Virtual Reality Systems.

In Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016) - Volume 1, pages 519-524

ISBN: 978-989-758-179-3

519

is not a new proposal. In the late 90s, Steve the

animated agent who played the role of an instructor

was representative (Johnson et al., 2000). At that time

(Lane and Johnson, 2008) pointed out that there were

still a number of unanswered key questions in the

literature of this technological pair, some of these are:

How distracting is explicit feedback? and What are

the risks of stealth guidance and experience

manipulation on learners with respect to confidence,

self-efficacy, and help-seeking skills. Virtual reality

carried out in its evolution and Burdea and Coiffet

(2003) consider that training is one of the main fields

for VR application.

Nowadays virtual reality technology enables

developers to offer more realistic environments.

Virtual environments are helpful for users to visualize

how physical activities should be realized.

Interactivity has improved but is still under research.

Augmented reality, another strand of virtual reality is

becoming more useful as training support in industry

(Carson, 2015). Wagner (2015) states virtual reality

will make online tutoring as common place as one-

on-one tutor over the next years; he even envisages

that online degrees are more affordable that the

traditional ones.

An analysis of virtual reality evidences its

potential for tutoring activities, since it allows the

integration of features such as interactivity,

visualization and audio, among others, to stimulate

different human learning channels (Pérez and

Ontiveros, 2009), which are propitious and helpful to

enhance the learning process.

Both technologies are still under research but

already mature enough to answer some of the

questions posed in the past.

This paper presents our general proposal for the

blended training model and describes their main

components.

2 BLENDED TRAINING MODEL

Training is a strategic activity in corporations as it is

recognized that the efficiency of organizations

depends directly on human capital, which in turn may

depend on adequate training. High productivity in

part is the result of efficient training, which becomes

even more valuable when there is risk of accidents

that harm people. Such is the case of electrical field,

which involve risk of electric shock, arc flash and

other hazards for people; also there may be potential

damage to equipment in electrical installations.

The training programs on the electrical field are

very detailed and strict. A trainee has to accredit the

classroom courses but he also must have camp

practice with the close supervision of an instructor. In

this traditional training method, the trainees spend a

lot of time and also the training becomes costly.

With these elements, we are developing a blended

training model to support traditional training as it can

be seen in Figure 1. In this training model, the trainees

learn through three elements: i) an instructor in

classroom courses, ii) an intelligent training system

and iii) camp practice.

Figure 1: Blended training model.

The aim of the blended training model is to have

efficient, fast and safe training, and also to reduce

training costs. The base of the blended training model

is the traditional training which in turn is based on a

plan including theoretical lessons and practice.

In a face-to-face interaction, the instructor teaches

trainees in classroom (first component). These

classroom classes are supported by the intelligent

training system (second component) where trainees

can reinforce the theoretical topics by executing

practices in a virtual environment. Separately,

trainees can learn and practice with the intelligent

training system as much as they want; this is in a self-

learning modality.

When trainees have attended the appropriate

courses they have to serve as auxiliary electricians to

have camp practice (third component) in a real

electrical installation.

The training course is planned by the instructor;

he decides which topics will be included in the course

and designs the course in the intelligent training

system. In classroom, the instructor explains

theoretical concepts and shares his experience in the

performance of electrical manoeuvres.

CSEDU 2016 - 8th International Conference on Computer Supported Education

520

This model, which supports self-learning, is

looking for adaptive training, where particular

trainee’ needs are considered. We have established a

road map with several phases to achieving such

training model (Hernández and Pérez, 2014).

3 INTELLIGENT TRAINING

SYSTEM

The intelligent training system is the component

which allows adapting the training to particular needs

of each trainee. It includes elements from intelligent

tutoring systems such adaption to particular needs by

means of a student model (Sottilare, 2013). Figure 2

shows a diagram of the intelligent training system.

Figure 2: Intelligent training system.

Besides attending the course, trainees practice the

electrical topics included in the course by using the

intelligent training system with an animated agent.

A key element of this intelligent system is the

trainee model that is built based on the interaction

trainee-system. Also the trainee model is updated by

the instructor considering the progress of the trainee

in class and his performance in camp.

The trainee model represents the knowledge and

affective states of trainees and their profile. The

information in this model is useful for instructors to

adapt the instruction in classroom, to plan the camp

practice, to recommend attending other training

courses or finally to grant a certification. The model

can be useful to design new courses and new testing

materials and even to redesign training material.

The trainee interacts with the intelligent training

system via a virtual reality system. This system

presents to trainees a virtual representation of the

electrical environment enabling the practice before

going to the real electrical installation. Also the

system allows a safe training since the trainees can

practice as much as they want without risk to injure

them or damage costly equipment.

Depending on the specific electrical topic, the

instruction and the practice in the intelligent training

system can be adapted to the progress and knowledge

of the trainee. In specific cases, it is difficult to adapt

instruction because the electrical manoeuvre has to be

performed sequentially. However, the intelligent

training system can suggest to reviewing specific steps

or to studying certain topics.

Another component is an empathic animated

agent which uses the trainee affect to present the

instruction properly. We are using the characteristics

of the operators for developing the agent, such as

wearing the uniform and safety helmet, among other

features. We believe that by representing the tutor as

an electrician, operators will accept better the training

environment. Empathy is the ability to perceive,

understand and experience others’ emotions, in other

words, to step into the shoes of another. This

construct has been incorporated in animated agents

with the aim to achieve credibility, social interaction

and user engagement (Hone, 2006).

The knowledge about the electrical field

composed by teaching and testing material are

designed and developed by a team of experts.

3.1 Pedagogical Trainee Model

The pedagogical trainee model represents the

trainee’s knowledge about electrical topics included

in the course. The model is updated when the trainee

practices the electrical manoeuvres and when he

solves theoretical exams. The model consists of a

Bayesian network (Sucar, 2015). The Bayesian

network is built when the instructor designs a course.

Figure 3 shows an example of a Bayesian network for

a course with five electrical topics. In turn each topic

is composed by a sequence of subtopics.

The Bayesian network is composed by a node for

each electrical topic included in the course. In turn,

each node of the Bayesian network representing a

topic is a Bayesian network composed by topics and

subtopics.

Initially, the nodes representing topics have two

possible values: learnt and not learnt and their

probabilities are conditionally dependent on the

probabilities of learning the subtopics nodes.

Course nodes also have two values: acquired and

not acquired and their probabilities are conditionally

dependent on the probabilities of knowing the topic

and subtopics nodes.

We are working on including a node for a

theoretical exam also represented by a Bayesian

network composed by a number of items. The causal

relationships between items and conditional

probabilities for each node will be established when

the exam is designed by the instructor. For the time

Adaptive and Blended Learning for Electrical Operators Training - With Virtual Reality Systems

521

being, we have not defined the complete structure and

values of this Bayesian network. However we want to

model trainee’ guesses and slips on the basis of the

relationships between the items and the evidence of

the answers to questions. Figure 4 shows an exam

with 8 items as a preliminary example.

Figure 3: Bayesian network for a course.

Figure 4: Initial Bayesian network for an exam.

3.2 Affective Trainee Model

The affective trainee model uses the OCC model

(Ortony, Clore and Collins, 1988) to provide a causal

assessment of emotions based on contextual

information. The OCC model defines emotional state

as the outcome of the cognitive appraisal of the

current situation with respect to one’s goals. The

trainee model consists of a dynamic Bayesian

network that probabilistically relates personality,

goals and interaction events with affective states.

Figure 5 shows a high level representation of the

model, where each node in the network is actually a

set of nodes in the detailed model. The model is based

on the proposal by Conati and Mclaren (2009) and in

our previous work (Hernández, Sucar and Arroyo,

2012).The dynamic Bayesian network models the

dynamic nature of emotions. To infer the affective

state, it considers the trainee’s knowledge,

personality, and the tutorial situation at that time, as

well as the previous trainee affective state. The

tutorial situation is defined based on the results of the

trainee actions.

The trainee’s appraisal of the current situation

given his goal is represented by the relation between

Figure 5: Dynamic Bayesian network for the affective

trainee model.

the goals and the tutorial situation nodes through the

satisfied goals node. The influence of the appraisal

process on the trainee’s affect is represented by the

link between the satisfied goals node and the affective

state node. From the complete set of emotions

proposed by the OCC model, the affective model only

includes six emotions: joy, distress, pride, shame,

admiration and reproach. We use only these emotions

because they are related to the events we want to

evaluate: the emotions joy and distress are reactions

by the individual to an event in the training session.

The emotions pride and shame emerge as a

consequence of the trainee’s action. The emotions

admiration and reproach emerge as a consequence of

the tutor’s action.

According to the OCC model, one’s goals are

fundamental to determine one’s affective state, but

asking the trainees to express these goals during

training would be too intrusive. Consequently, the

goals in our network are inferred from personality and

trainee’s knowledge.

3.3 Animated Agent

Training activities are presented to trainees through

an animated pedagogical agent. These agents

represent a major trend to have a more natural human-

computer interaction (Breese and Ball, 2008,

Johnson, Rickel and Lester, 2000). Animated

pedagogical agents interact face to face with the

students through facial expressions, gaze, emotions

and deictic gestures; and cohabit with the students

learning environments. Animated pedagogical agents

have a significant impact on training systems as they

give the impression that someone is on the other side

(Sagae et al, 2012); thus the trainee perceives a very

different behavior from a traditional system and more

alike to human behavior. Among the behaviors of an

animated pedagogical agent are those typical of

intelligent tutoring systems, but there are some

Course

To p ic 1

Topi c 2

Topi c 3

Topi c 4

Sub

topic 1

Sub

topic 2

Sub

topic 3

Sub

topic 1

Sub

topic 2

Sub

topic 3

Sub

topic 4

Sub

topic 1

Sub

topic 2

Sub

topic 3

Sub

topic 4

Sub

topic 7

Sub

topic 5

Sub

topic 6

Sub

topic 1

Sub

topic 2

Sub

topic 3

Exam 1

Item

Satisfied Goals

Knowledge

State

Goals

Personality traits

Tutorial

Situation

Affective state

Satisfied Goals

Goals

Personality traits

Affective state

n+1

Knowledge

State

Tutorial

Situation

CSEDU 2016 - 8th International Conference on Computer Supported Education

522

particular of these characters, such as demonstrations

of complex tasks, observe and assist the trainee to

perform their tasks, in addition to guiding trainees in

virtual spaces (Wang et al, 2008).

We are using the characteristics of the operators

for developing the agent, such as wearing the uniform

and safety helmet, among other features. We believe

that by representing the tutor as an electrician,

instructors and trainees will accept the training

environment. We have conducted a study to evaluate

the design of the empathic agent and gather

knowledge to refine it. We obtained encouraging

results, as the electricians welcomed the agent

(Hernández et al, 2016). The results of the study shed

some light to refine the facial expressions of the agent

and its overall design. Figure 6 shows two instances

of the animated agent.

Figure 6: Animated pedagogical agent.

In this initial phase, the animated agent will

deploy the emotions recognized in the trainee base in

the OCC model as described above. The facial

expressions, consequence of the emotions, adopt the

theory proposed by Ekman and Friesen (1978). We

are trying to accomplish an empathic behaviour in the

animated agent to achieve believability and user

engagement, and in turn to improve learning.

4 VIRTUAL REALITY SYSTEMS

We have developed different non immersive virtual

reality systems for training. ALEn

is one of them

and nowadays is a complementary training tool for

medium tension live-line maintenance. In fact there

are different versions of this system, all devoted to

maintenance of energized lines. Thus, we have

ALEn

MT for medium tension power lines, See

Figure 7, ALEn

AT for high tension power lines

and ALEn

LS for underground power lines.

Besides these systems we have also developed a

virtual reality system for protections maintenance, see

Figure 8, and substation tests. All these systems share

in some degree the same architecture and

functionality within different instructional domains.

They keep track of trainees’ progress; however we are

still working on them to integrate some intelligence,

so that these systems are able to keep fully records of

the model of trainees and even integrate the animated

agents and capability to recognise emotions among

other functionalities proposed in the blended training

model.

Figure 7: Virtual reality training system for medium tension

live line maintenance ALEn

MT.

Figure 8: Virtual reality training system for maintenance

tests to protections.

5 CONCLUSIONS

In this paper we propose a blended model for training

electricians. This model includes an intelligent

training providing adaptive and intelligent training

since it recognizes the affect and knowledge state of

trainees. The instruction is presented in a proper way

by an empathic agent who is a learning companion for

the trainee. The intelligent training system integrates

a virtual reality system.

We have presented the characteristics of the

intelligent training system as a component of the

blended training model, i.e. learning and practice is

part of a course; however trainees also can use the

intelligent training system as distance self-training

tool, practicing any topic at any time.

Adaptive and Blended Learning for Electrical Operators Training - With Virtual Reality Systems

523

Even though we have added different

technologies to our model and training systems in

order to make them efficient, still presence of human

instructors plays a decisive role. These technologies

are helpful tools to support and improve training but

cannot substitute instructor. As in other fields,

training within the electrical field often involves high

risk activities where mistakes are usually fatal.

Thus, the intelligent training system is a helpful

complementary training tool which can be used to

enhance the traditional training but it cannot be used

instead of it.

As future work we are planning to show the

trainee model to trainee as a self-evaluation tool. Self-

assessment is one of the meta-cognitive skills

necessary for effective learning. Trainees and

students, in general, need to be able to critically assess

their knowledge in order to decide what they need to

study (Mitrovic and Brent, 2002). For the time being

the open trainee model is used only by instructors.

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