An Authoring Tool based on Semi-automatic Generators for Creating

Self-assessment Exercises

Nathalie Guin

and Marie Lefevre

Univ. Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622 Villeurbanne, France

Keywords: Self-assessment, Semi-automatic Generation of Exercises, Authoring Tool, University Context.

Abstract: This article presents ASKER, a tool for teachers to create and disseminate self-assessment exercises for their

students. Currently used in the first year of a bachelor's degree at the University of Lyon (France), it enables

students to carry out exercises in order to evaluate their acquisition of concepts considered important by the

teacher. ASKER enables the creation of exercises (matching, grouping, short open-ended questions, multiple

choice questions) that can be used to assess learning in many different fields. To create exercises to assess a

concept, the teacher defines a model of exercises that will enable the generation of various exercises, using

text or image resources. Such an exercise model is based on constraints that the exercises created from this

model must comply with. Automatic generators create, from the resources defined by the teacher, many

exercises respecting these constraints. The possibility for the learner to request the generation of several

exercises from the same model enables her to assess herself several times on the same concept, without the

teacher having to repeatedly define many exercises.

1 INTRODUCTION

The purpose of the work described in this article is to

enable a learner to assess her knowledge within a

learning path for which a teacher defines learning

objectives. Our approach is to enable the teacher to

provide the learner with exercises on the concepts to

be acquired. The learner can use these exercises if she

wishes to evaluate her mastery of the concepts

involved in the exercises. We therefore place

ourselves here in the context of a formative

evaluation.

Self-assessment with immediate feedback

requires activities or features that allow students to

assess themselves against the course objectives.

Exercisers (or exercise generators) are a way to

quickly diagnose the skills acquired, to perform

performance memorization and skill development

through trial and error learning based on repetition

(Mostow et al., 2004). Since the exerciser

environment keeps track of the learning activity and

provides immediate feedback, it facilitates the

learner’s regulation by allowing explicit reflection on

the skills worked on (Steffens, 2006).

https://orcid.org/0000-0001-9999-9878

https://orcid.org/0000-0002-2360-8727

We propose an authoring tool that gives the teacher

the freedom to set the notions on which learners can

assess themselves, and that enables him or her to

create exercises to assess the mastery of these notions.

In order to meet the needs of as many people as

possible, we have chosen to consider types of

exercises that can be applied to many fields, such as

MCQ (Multipe Choice Questions), matching,

grouping, ordering, gap texts and so on.

The following section explains why we have

chosen an approach based on exercise generators to

address this issue of creating self-assessment

exercises. We then specify the knowledge acquisition

processes necessary for this approach, before

presenting the architecture of the tool we have

developed: ASKER (Authoring tool for aSsessing

Knowledge genErating exeRcises). Finally, after

having shown how this tool can be used by both

teachers and learners, we carry out an evaluation of

the use of ASKER in first-year bachelor’s degree

courses at the University of Lyon.

Guin, N. and Lefevre, M.

An Authoring Tool based on Semi-automatic Generators for Creating Self-assessment Exercises.

DOI: 10.5220/0010996100003182

In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 181-188

ISBN: 978-989-758-562-3; ISSN: 2184-5026

181

2 AN AUTHORING TOOL BASED

ON EXERCISE GENERATORS

Our aim was to enable a teacher to offer learners

online self-assessment exercises. Such exercises

enable learners to autonomously test their level of

mastery of what they have learned in the course,

whether this course is online or face-to-face. Learners

may fail in initial attempts to respond to exercises if

knowledge is not mastered. It is therefore possible

that a learner may have to answer several times to the

same exercise before achieving success. In order for

the learner not to be influenced by her previous

resolutions, it is necessary that the self-assessment

exercises be different from one time to another, while

evaluating the same knowledge. However, it does not

seem reasonable to ask the author to write many

versions of the exercise. That's why we have chosen

to use exercise generators that the author can easily

use in any field. However, we designed a semi-

automatic process of generating exercises, in order to

let teachers take part in the choice of the criteria that

the exercises will have to meet.

To meet the needs of teachers and learners, the

expected properties of the authoring tool were as

follows:

- The exercise is different from one time to another.

- The author is in control of the content of the exercise

and is assured that it meets his or her expectations

in terms of educational content.

- Exercise generators can be used in a wide range of

fields and grade levels.

- The diagnosis of the response is made automatically

and in real time.

- The construction of exercises with generators is a

time saving for the author compared to a creation

exercise by exercise.

- The creation of exercises does not require technical

skills.

Many researches have studied the question of

authoring tools in the field of Technology Enhanced

Learning, and several literature reviews about the

topic were published (Murray, 1999) (Murray, 2003)

(Woolf, 2010) (Dermeval et al., 2016). In these

works, the objective is to create Intelligent Tutoring

Systems. For our part, we only want to enable the

creation of training and self-assessment exercises,

which is why we focus our study on exercise

generators. As we also want our authoring tool to be

usable in any field, we have not integrated a model of

https://articulate.com/360/studio#quizmaker

http://hotpot.uvic.ca/

https://www.claroline.com/

the knowledge and skills to be assessed. Indeed, our

goal is that a teacher can directly use the tool to create

exercises without the need for a prior knowledge

modeling phase. We thus agree with Baker's approach

(Baker, 2016), which, noting that ITS used at scale

are not the most intelligent ones, proposes to design

simpler tools that support teachers' activity by

enhancing their expertise.

Existing exercise generators can be categorized

into three categories: manual, automatic and semi-

automatic.

Often used as part of authoring tools, manual

generators give a great deal of freedom to the author,

which precisely defines the content of the exercise

and all the formatting options. Some commercially

available authoring tools such as Articulate

Quizmaker

or Hot Potatoes

are commonly used to

create paper-based or computer-based exercises. The

online learning platforms Claroline

and Moodle

commonly used in higher education, also offer their

own exercise editing tools. With this type of

generators, the author is guaranteed to have an

exercise that precisely matches his/her expectations,

which meets one of our needs. On the other hand, the

author must create each instance of exercise one by

one, specifying its contents. This type of generator is

not able to automatically create a large number of

exercises assessing the same skill.

Automatic generators do have this ability, but

unfortunately leave little space for the author in the

creation process. With this kind of generator, a large

number of exercises are created automatically

without the author being able to influence the

system's choices. He or she can simply choose the

category of the exercise (form, theme, knowledge

addressed) but cannot act on the content or on specific

constraints. Examples include the Reading Tutor

generator (Mostow et al., 2004), or the Aplusix

generator (Bouhineau et al., 2008).

In order to take advantage of the features of the

automatic generators while leaving to the author the

editorial freedom on the exercises created, we have

chosen semi-automatic generators, which combine

the advantages of the two previous categories. These

generators propose that the author define a model of

exercises, which is then instantiated to produce a

large number of exercises (Jean-Daubias and Guin,

2009) (Delozanne et al., 2003). Some e-learning

platforms like Moodle, Wims

, WeBWorK

(Auzende et al., 2007) offer exercises involving

http://moodle.org

https://wims.univ-cotedazur.fr/wims/

http://webwork.maa.org/

CSEDU 2022 - 14th International Conference on Computer Supported Education

182

variables that are similar to the concept of an exercise

model. This type of generator partially meets our

needs but is limited to the areas requiring calculation.

They are used for fields such as mathematics and

science, and not all of them are accessible to non-

programmer authors.

In order to have semi-automatic generators

adapted to many domains and including other types

of exercises, we have chosen to use the GEPPETO

approach (Lefevre et al., 2012). This approach

consists of enabling the teacher to express constraints

on the exercises to be generated. To do this, it is

necessary to have a model of the types of exercises

that can be generated, in order to know the type of

constraints that the teacher must be able to express.

The following section thus specifies the models

guiding the acquisition of the knowledge necessary to

generate exercises according to the GEPPETO

approach.

3 ACQUISITION OF

KNOWLEDGE FOR

GENERATING EXERCISES

Figure 1 presents the GEPPETO approach (GEneric

models and processes to Personalize learners'

PEdagogical activities according to Teaching

Objectives). This approach enables the acquisition of

knowledge at several levels, from experts and

teachers, to generate exercises.

In GEPPETO, a meta-meta-model of exercises

was defined by the research team (Lefevre et al.,

2009). This model specifies the knowledge that an

expert will need to define in order to create a meta-

model of exercises of a given type (see A in Figure

1), for example a meta-model of exercises of the

MCQ type, or of the matching type.

This meta-model of exercises of type X or Y then

enables the teacher to specify constraints defining a

model of exercises (cf. B in Figure 1). Depending on

the type of exercises, the constraints will not be the

same, so that’s why the exercise meta-model is

needed. For example, the teacher could use the meta-

model of the MCQ-type exercises to define a model

of MCQ-type exercises covering a given subject and

containing N questions, with for each M propositions

with only one correct answer.

Using such exercise models, exercise generators

can construct several exercises conforming to these

models (see C in Figure 1). The exercise generators

able to use these models depend on the type of

exercises, and therefore on the meta-model of

exercises of type X or Y.

It can therefore be seen that the GEPPETO

approach requires two knowledge acquisition

processes:

- Acquiring the expert's knowledge for the creation of

meta-models of exercises (see A in Figure 1). This

acquisition is done only once for each type of

exercises and is based on the meta-meta-model of

exercises.

- The acquisition of the teacher's knowledge for the

creation of the exercises models (see B in Figure

1). This acquisition is carried out on a regular

basis for the construction of various exercise

models and requires an interface that is based on

the meta-model of exercises of a given type (the

constraints that the teacher must define depend on

the type of exercises).

Figure 1: The GEPPETO approach.

An Authoring Tool based on Semi-automatic Generators for Creating Self-assessment Exercises

183

Meta-models and the meta-meta-model are

independent of the field for which an exercise will be

generated. For example, the meta-model

Identification of text parts specifies how to formulate

the guidelines, how to characterize the different text-

based resources, and how to describe the actions to be

carried out on these resources to generate exercises.

An exercise generator is associated to each of

these meta-models. An interface associated with the

generator and based on the meta-model enables the

teacher to define constraints on the exercises to be

generated. It is at this point, when creating the model

of exercises (for example, a gap text where it is

necessary to put the verbs in the past), that the

application to a field and a level of study will be

carried out. The exercises generated from the exercise

model are therefore, of course, dependent on the field.

Since the meta-models are all consistent with the

meta-meta-model, all the exercise generators share a

common architecture (Lefevre et al., 2009).

4 ASKER TOOL

ARCHITECTURE

The GEPPETO approach was designed to create

paper-pencil exercises. To design an authoring tool to

provide learners with online exercises and immediate

diagnosis, we chose to follow the same approach. Our

research hypothesis was that using constraints on the

exercises to be generated allowed both to obtain a

sufficient variety of exercises for learners to train and

self-assess, while requiring less work for the author

teacher.

Thus, in the ASKER authoring tool, we have

chosen a model inspired by the GEPPETO approach:

after having chosen a type of exercise (MCQ,

matching, gap text...), the author can create an

exercise model that describes the content and form of

the exercises he or she wants to create, but without

necessarily constraining it completely. By exploiting

this model, an exercise generator is able to provide

the learner with a large number of different exercises

evaluating the same skill. Each exercise instance

generated in this way will be interactive: the learner

will respond online and obtain a diagnosis of her

response.

The types of exercises we selected are:

identification of parts of the text (includes gap text),

organization of elements (ordering, grouping,

matching), annotation of illustrations, Multiple

Choice Questionnaire, open and short-ended

questionnaire.

Figure 2 shows the architecture of the ASKER

authoring tool. The upper block is made up of the

different levels of representation of the exercises,

resulting from the GEPPETO approach: the meta-

models of types of exercises, the models of exercises,

and the instances of exercises. In the central block are

the three mechanisms that manipulate these

representations of exercises. The lower block

contains the resources used in the exercise creation

process.

Resources are the basic elements that are used to

build exercises, for example texts, images or element

sequences. Each resource has metadata characterizing

it to facilitate searches (theme, level, etc.) as well as

metadata enriching the resource to define exercises,

such as image captions or annotations on image areas.

For example, the author can define image type

resources, with flag images, and define for each flag

image its country, its capital city, and its continent.

Figure 2: ASKER tool architecture.

CSEDU 2022 - 14th International Conference on Computer Supported Education

184

The author chooses a type of exercises T (for example

matching). Let’s call MMT the meta-model of the T-

type exercises described by an expert. The author

creates a model of exercises (let’s call it MT)

compliant with MMT using a dedicated interface

based on the knowledge contained in MMT about the

type of exercises T. This MT model defines a set of

constraints that the exercises resulting from this

model must respect. For example, it is a 5 pair

matching exercise, choosing flag images, and

matching each image with its "country" metadata.

The author can generate some instances exercises of

MT (let’s call them ExoT) to test if the model does

give rise to the expected exercises

The T-type exercises generator associated with

MMT (so here the matching exercises generator)

therefore receives an input model of exercises MT

that it instantiates to produce an output ExoT

exercise. The ExoT exercise is consistent with the MT

model and therefore with the choices of the author

who created it. The generator requires no human

intervention. It has all the necessary information in

the MT exercise model and makes use of resources

(here the flag images). The generator is used

whenever you want to obtain a new ExoT instance of

exercise (which contains the diagnosis) from the MT

model.

The exercise is then presented to the learner via a

resolution tool that formats the exercise, gathers the

learner's answer and provides a diagnosis. In our

example, the generated exercise will propose 5 flags

to the student, and the student will have to find the

country of each flag. The variety of exercises

generated will therefore come in this example from

the amount of flag images available in the resources.

The same applies to exercises using texts. Variety can

also come from the use of formulas using variables

whose values must be chosen within an interval

defined by the author. The resources can be used for

different exercise models. For example, we could

define an exercise model where the capital associated

with each flag must be found. Or a categorization

exercise where flags must be put in boxes

corresponding to their continent.

5 THE ASKER TOOL

The ASKER (Authoring tool for aSsessing

Knowledge genErating exeRcises) platform can be

used on the one hand by a teacher to create models of

exercises, and on the other hand by learners to carry

out exercises generated from these models and to

obtain a diagnosis for self-assessment.

An Authoring Tool for the Teacher. ASKER

currently enables a teacher to create models of

exercises of type MCQ, short open-ended questions,

matching, grouping and ordering. The teacher can

create resources such as texts, images, MCQ

questions, short open-ended questions. On each of

these resources, he or she can add meta-data that will

be used by the exercise models using the resource. A

resource can be used for several exercise models of

different types. Thus, the same meta-data on a

resource can be used for both a matching, grouping or

ordering exercises.

Figure 3: Author’s interface for creating a model of matching exercises.

An Authoring Tool based on Semi-automatic Generators for Creating Self-assessment Exercises

185

Figure 3 shows the author's interface for creating a

model of matching exercises. The author selected

text-based resources (right-hand side of Figure 3) and

filtered out those for CC (chapter) number 3 of UE

LIF3 (the name of the teaching unit). Several texts of

functions in Scheme programming language can be

used. On these texts, meta-data specifies the

specification of each function, its input type(s) and its

output type.

To create an exercise model, the author defines

(left part of Figure 3) that he or she wishes to create

exercises in which 4 functions must be associated

with their specification. The exercises generator then

uses this exercise model to create exercises that meet

these constraints, using the available resources

describing functions. Another model of exercises

could use these same resources describing functions

but asking to group them by input type or output type.

In this way, the same resources can be used to

produce another type of exercise and related to

another notion of the course.

A Self-assessment Tool for the Learner. The

teacher suggests to his or her students models of

exercises corresponding to the concepts studied in

class. For each exercise model, the learner can request

the generation of several instances of exercises. She

then resolves a first exercise derived from the

exercise model, next the system diagnoses her

answers and displays a feedback (in green and red) on

her answers as well as the correct answer (in blue) to

the exercise (see Figure 4).

A commentary prepared by the teacher explaining

a common error or reminding an important concept

may also be displayed. The learner can then revise the

course if necessary and ask for a new exercise based

on the same exercise model.

6 EVALUATION OF THE ASKER

TOOL

At the University of Lyon, in the first year of a

Computer Science degree, there are two initial

courses in programming: one on imperative and

iterative programming in C language, the other on

functional and recursive programming in Scheme

language.

We set up the use of the ASKER tool for the

Scheme programming course several years ago. It is

a use of ASKER for a teaching that is not digitized,

the platform being a complementary tool to face-to-

face teaching. We suggested that the students use

ASKER to self-assess their understanding of the

concepts presented in class before coming to the

supervised works. This enables students to situate

themselves in relation to the acquisition of the notions

covered in courses, to prepare the assessments carried

out each week in supervised works, and to diagnose

their difficulties.

For this purpose, we have proposed a set of model

exercises for each of the 9 lectures. By instantiating

the meta-exercise models, we provided students with

18 models of matching exercises, 9 models of

grouping exercises, and 8 models of MCQs. The

creation of these 35 models and their 121 resources

represented between 1 to 2 hours of work each week,

during the 12 weeks of teaching, for the teacher in

charge of the fall semester. This is a considerable

workload, but it only concerned the initialization

phase. These different models and their resources

were then exploited and completed by the spring

semester teacher in a more reasonable time: 1/2 hour

per week. Since then, the use of ASKER in this course

does not require any more time for the teacher.

Figure 4: Feedback provided by ASKER to the learner on her resolution of several types of exercises.

CSEDU 2022 - 14th International Conference on Computer Supported Education

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We did not consider evaluating the quality of the

exercises generated in terms of their impact on student

learning. Indeed, our objective being to provide the

teacher with a tool enabling her or him to generate

varied exercises in sufficient number, we consider that

the tool fulfils its objective if the exercises generated

are in accordance with the teachers' expectations,

which is the case with our approach of constraints

defined by the teachers and satisfied by the generators.

We then introduced the use of ASKER in the other

first year course, devoted to algorithms and

programming in C. The teacher has created 37

exercise models, divided into 6 chapters. Of the 584

students enrolled in the course, 485 (83%) used

ASKER at least once. For each model planned by the

teacher, the number of students who did at least one

exercise from this model was around 46%.

In order to measure the impact of the platform on

students, we could not for ethical reasons conduct a

comparative experiment, giving access to ASKER

only to a part of the students. A questionnaire was

distributed at the end of the semester to students

enrolled in the course. We received 106 responses

from students who used the platform as this:

- 67% of students did exercises each week, 23% every

other week, 9% at the beginning of the semester

but no more afterwards.

- At each use, 43% used it between 5 and 10 minutes,

43% between 10 and 20 minutes.

- 82% of students generated multiple instances of

exercises from the same model.

- Students mainly used it to study before the

supervised works sessions (83%). They each time

made all the models of exercises proposed for a

chapter (82%).

- Students reported that ASKER helped them to

understand the course (58%) and to identify

concepts not understood (63%). 89% of them

think that using ASKER has enabled them to

progress in this course (70% a little and 19% a lot).

Although this is not an evidence of ASKER's impact

on learning, students think that this tool has had a

positive impact on their learning. Students are not

under any obligation to use ASKER during their first

year. Their use of the tool is in no way used to evaluate

their work. The tool is just available if they want to

use it and many use it all year round. Usage statistics

and student comments also show that this tool

increases their motivation to work, which in itself is

already a very satisfying result.

ASKER has also been used by 3 physics and

chemistry teachers at high school. These teachers used

to offer their students paper-based exercises to enable

them to self-assess certain skills. They wanted to use

ASKER to produce similar exercises that would give

their students immediate feedback on their answers.

This immediate feedback was a demand for 75% of

their students.

After a while of hands-on learning, these teachers

were able to use ASKER to create 44 exercise models

for their students. They have made extensive use of

image-type resources. They appreciated the

opportunity to have a competency used in various

situations (due to the variety of resources) and to

create exercises involving different cognitive tasks

(due to differing types of exercises). Using ASKER

also gave them the idea of new exercises compared to

those they used on paper. Their students loved the

application, and in particular the immediate feedback.

They have asked to be able to use ASKER in all

chapters of their Physics and Chemistry courses.

To meet the self-assessment requirements that we

formulated in Section 2, our tool had to have the

following properties:

- The exercise is different from one time to another.

This property is satisfied thanks to the generators

using constraints set in the exercises models. The

practice shows that students actually do several

exercises for each exercise model (82% of them).

- The author is in control of the content of the exercise

and is assured that it meets his or her expectations.

This property is satisfied with the authoring tool

that enables the teacher to create an exercise model

specifying the constraints that the exercises must

satisfy, and enable him/her to control the exercises

generated from each model.

- Exercise generators can be used in a wide range of

fields and grade levels. ASKER has been used in

computer science, optics and anatomy at

university, physics and chemistry at high school,

but also to evaluate schoolchildrens' knowledge of

countries around the world or to generate IQ tests

based on logical sequences.

- The diagnosis of the response is made automatically

and in real time. The models of exercises defined

by teachers include the knowledge necessary for

generators to diagnose student responses.

The construction of exercises with generators is a

time saving for the author compared to a creation

exercise by exercise. Although the definition of

exercise models takes time, especially at the

beginning, teachers appreciate the variety of

exercises generated by using annotated images

and texts or calculation formulas. Creating such a

variety of exercises without generators would take

too much time.

- The creation of exercises does not require technical

skills. In both programming courses, the teachers

An Authoring Tool based on Semi-automatic Generators for Creating Self-assessment Exercises

187

who have used ASKER are computer scientists,

but the use of ASKER does not require computer

skills. In other uses, the authors were professors of

physics, chemistry or optics. The latter has taken

charge of this tool in complete autonomy.

To conclude, all the feedback from the use of ASKER

in different contexts allows us to consider that the tool

meets the needs of both teachers and learners.

7 CONCLUSION AND

PROSPECTS

In this article we introduced ASKER, a tool that

enables teachers to create self-assessment exercises

for their students. This tool can be used for distance

learning or as a complement to face-to-face teaching.

It enables the creation of exercises (matching,

groupings, short open-ended questions, MCQ) that

can be used to evaluate learning in many fields. To

create exercises to assess a concept, the teacher

defines a model of exercises that will enable the

generation of various exercises, using text or image

resources. The possibility for the learner to request

the generation of several exercises from the same

model enables her to self-assess repeatedly on the

same concepts, without the teacher having to

repeatedly define many exercises.

Our research hypothesis was that using

constraints on the exercises to be generated allowed

both to obtain a sufficient variety of exercises for

learners to train and self-assess, while requiring less

work for the author teacher. The evaluation results,

reported in Section 6, allow us to validate this

research hypothesis.

ASKER is a tool that can be used in a variety of

fields, and in a variety of learning contexts, at any

level. It thus offers many possibilities of use. Its main

limitation is that there is no explicit representation in

ASKER of the knowledge to be learned. The

acquisition of this knowledge therefore represents a

major challenge. The main users of ASKER being the

authors, it would be interesting for them to build the

domain knowledge, as they already do for formulas.

The system could assist them in this task by proposing

a generalization of the information that they provide

to create their models of exercises. We intend to use

activity traces of teachers using ASKER to enable the

system to assist them in this elicitation of domain

knowledge.

We also envisage the use of a particular meta-data

describing the skills mobilized by a model of

exercise, so that we can propose to the learner an open

profile of skills that will enable her to be more

involved in her self-assessment process, for example

by setting objectives to be achieved. Such skills

profiles will also enable us to propose to the student a

learning and training path that will enable her to

achieve such objectives.

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