SUPPORTING THE IDENTFICATION OF TEACHERS’
INTENTION THROUGH INDICATORS
Aina Lekira, Christophe Després and Pierre Jacoboni
Computer Science Laboratory (LIUM), Université du Maine, Avenue Laënnec, Le Mans, France
Keywords: Teaching intention, Teachers’ self-regulation, Indicators, Learners’ activities regulation, Teachers’ activities
instrumentation, Hop3x.
Abstract: In this paper, we deal with the instrumentation of teachers’ activities: the regulation of learners’ activities
and their self-regulation. Indeed, this latter is essential in order to have better learning effects during
learning sessions. Supporting teachers self-regulation implies giving them information about the real impact
of their work, i.e. do the effect of their interventions meet their initial intention. Here, the focus is on the
identication of the latter. To do this, we adopt a declarative approach and rely on indicators. Moreover, to
assess our proposition, Hop3x, a TEL system, was designed and a pilot test was carried out.
1 INTRODUCTION
Our work deals with Technology-Enhanced
Learning (TEL) research field; it aim specifically at
supporting teachers in their activities.
Studying instrumentation issues and more
precisely instrumentation of teachers’ activities
consists most of the time in proposing models and
tools (Martinez et al., 2003) (Kazanidis et al., 2010)
which allow them to regulate learners’ activities.
Research outcomes most often led to tools designs
which offer teachers a visualization of indicators
(ICALTS, 2004) thus giving teachers information
about learners’ progress (Després, 2003),
productions (Lefevre et al., 2009) and elements
about the conditions in which tasks are carried out
(ICALTS, 2004).
In addition to the support of learners’ activities
regulation, ICALTS JEIRP (ICALTS, 2004)
considers that when teachers are involved in these
situations of instrumented tutoring, they need to be
aware of their own actions, activities and process in
order to evaluate them. Thus, giving teachers
information about their self-regulation: (1)
encourages them to have a reflexive approach about
their tutoring practices, choices and teaching
strategies (ICALTS, 2004), (2) allows them to
reconsider their teaching and pedagogical beliefs
(Benbenutty, 2007) and (3) leads them to refine their
practical experiences, improve their skills
(Capa-Aydin et al. 2009) and simply be more
efficient in their work (Zimmerman, 2000).
Supporting teachers’ self-regulation (awareness
and assessment) is essential because the challenge
related to learners’ regulation improvement is made
through the improvement of teachers’ self-regulation
and consequently results inbetter learning effects
(ICALTS, 2004). Such is the basis of our work. It
implies giving teachers information about the effects
of their actions, especially their interventions during
learning sessions.
In order to carry out this support, this paper will
focus on identifying their teaching intentions. The
aim is to know what makes them intervene in order
to give them information about the effects of their
interventions by checking the correspondence
between their original teaching intention and the real
effects of their actions.
Our discussion will proceed as follows: section 2
presents our work background and its general issue.
Section 3 describes Hop3x, the TEL system
designed and used in our work. The pilot test is
described in section 4. Its results are presented in
section 5 and discussed in section 6. Finally, we end
the paper by a conclusion and an outlook.
111
Lekira A., Després C. and Jacoboni P..
SUPPORTING THE IDENTFICATION OF TEACHERS’ INTENTION THROUGH INDICATORS.
DOI: 10.5220/0003343901110116
In Proceedings of the 3rd International Conference on Computer Supported Education (CSEDU-2011), pages 111-116
ISBN: 978-989-8425-50-8
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
2 BACKGROUND AND
GENERAL ISSUE
2.1 Description of Teachers’ Activities
This subsection presents the processes that teachers
have to manage during learning sessions: the
regulation of learners’ activities and the regulation
of their own tutoring activities (teachers’ self-
regulation) (Fig.1). These processes are deduced
from the adaptation to our work of Bandura and
Zimmerman’s socio-cognitive approach of self-
regulation (Lekira et al., 2009).
The regulation process is preceded by a phase,
which prepares the learning session. This
preparation phase consists in explicitly defining the
observation needs related to teaching objectives and
planning strategies to achieve them. The regulation
process of learners’ activities takes place during the
learning session; it is cyclical with threefold phases
defined as follows:
a phase of observation in which teachers
monitor and supervise learners’ work.
a phase of evaluation in which teachers
check if what learners do corresponds to the
objectives of the given activity and tasks.
a phase of reaction in which teachers
intervene or not, and adopt a remediation
strategy guided by a teaching intention.
Teachers’activities
Observation Evaluation Reaction
Sel
f
reaction
Sel
f
observation
Sel
f
evaluation
Remediation
strate
g
ies
Teaching
intention
Learners’
Activities
Regulationoflearners’
activities
Figure 1: Description of teachers’ activities.
The teachers’ threefold self-regulation process is
also cyclical and is defined as follows:
a phase of self-observation in which teachers
observe the effects of their interventions.
a phase of self-evaluation in which teachers
check if their interventions have reached the
expected effects and thus meet their initial
teaching intentions.
a phase of self-reaction in which teachers
validate their interventions or reconsider them
in adopting new remediation strategies.
2.2 Instrumentation of Teachers’
Activities
Our goal is to support teachers in their work by
offering them instruments to better carry out their
tutoring. To do this, we try to give them tools for
each step of the processes they have to manage
during learning sessions.
To reach this goal, we rely on indicators which
are at the core of our work. We here adopt the
ICALTS JEIRP (ICALTS, 2004) definition of an
indicator as a “ variable that describes ’something’
related to the mode, the process or the ’quality’ of
the considered ’cognitive system’ activity; the
features or the quality of the interaction product; the
mode or the quality of the collaboration, when
acting in the frame of a social context, forming via
the technology-based learning environment”. An
indicator has attributes such as name, value, etc.
As seen in Tab.1, in which we suggest the
possibilities of teachers’ instrumentation (a) during
learners’ activities regulation and (b) during their
own self-regulation, we propose to give teachers
indicators about learners’ work during the phase of
evaluation. These indicators are designed thanks to
the activity objectives. In fact, they meet teachers
needs of synthetic information at a more abstract
level. It enables to quantitatively and qualitatively
determine learners’ work without having to explore
the detailed tracks (Labat, 2002).
Moreover, during
the reaction phase, teachers also rely on the values
of these indicators to intervene.
During the phase of self-observation, the
monitoring tool we want to offer teachers allows
them to visualize and follow the variations of the
values of these indicators. Finally, in the phase of
self-evaluation, they are the witness of the effects of
teachers’ intervention through their positive or
negative evolution.
2.3 The General Issue
Based on our model of learners’ activities regulation
and teachers’ self-regulation, supporting teachers’
self-regulation means measuring the real impact of
teachers’ actions, i.e. following teachers’ reaction
effects through indicators and see if they meet
teachers’ original intentions. In order to give them
feedback about the effects of their interventions and
CSEDU 2011 - 3rd International Conference on Computer Supported Education
112
Table 1: Teachers’ instrumentation possibilities.
Phase Possibilities of instrumentation
Regulation of learners’ activities
Observation
A monitoring tool enables teachers to supervise
and follow the progress of learners’ activities
(productions, trails, etc.)
Evaluation
A comparison module compares the real values
of indicators to their expected values. This
module supplies teachers with synthetic
information about learners’ activities thanks to
indicators related to activity objectives and
calculated from learners’ tracks.
Reaction
A module obtains and identifies teachers’
intentions (i.e. what makes them react) in order
to support their interventions.
A communication tool allows teachers to
intervene according to the values of indicators
that they considered critical in the evaluation
phase.
Teachers’ self-regulation
Self-observation
A self-monitoring tool provides teachers with a
follow-up tool to keep track of their
interventions (self-monitoring) and the
conditions that surround them.
Self-evaluation
A comparison module provides teachers with
outcomes and gives them information about the
effects of their interventions. They can thus
assess the effects of their actions, performance
and progress in achieving their goals.
Self-reaction
A tool allows teachers to validate one
intervention (by giving them the intervention
performance) or to reconsider it by adjusting
their strategies and adopting a new one to ensure
the achievement of their goals via a
communication tool.
to support their self-evaluation and self-reaction, we
need to know why they react. In other words, we
want to know what makes them intervene, i.e. we
want to identify their teaching intentions.
Getting this teaching intention can be done
automatically or in a declarative way by asking
teachers to declare it. Attempting automatic
identification of teaching intention has an advantage:
its transparency for teachers. But on the other hand
the main disadvantage is the difficulty to detect it
precisely because there are a lot of elements to take
into account such as learners’ profile, their
knowledge and competence level, the type of tasks
or activity in which they are involved, their learning
style, and so on. Thus, this automatic detection leads
to a high error rate in identifying the teaching
intention. Then, the risk of cascading errors is very
high and it is not acceptable in what we want to do
(supporting teachers’ self-regulation) because
identifying teaching intention is the base of the
support of teachers’ self-observation, self-evaluation
and self-reaction.
Thus, we chose to study the declarative way
because teachers’ declarations of their intentions are
likely to have a low error rate: indeed, they identify
their intentions themselves.
As a matter of fact, some issues arise from this
solution: (1) The risk of adding an activity to the
activity, i.e. to increase teachers’ workload. Then,
they may find it too constraining and refuse to give
this information. (2) Teachers may declare teaching
intentions which do not correspond to the content of
their interventions.
In order to study these issues which deal with
getting and identifying teachers’ intention, our
approach consists in relying on indicators. As said
above, they are at the core of our work and we
consider them as the main causes of teachers’
interventions. But we are also aware of the fact that
during learning sessions, other elements come into
play and can affect the decision to intervene. In fact,
teachers can take into account elements such as
learners’ profiles, knowledge and competence level,
the kind of tasks or activity in which they are
involved and so on.
To reach our goal (getting and identifying
teaching intentions), we have designed a tool, which
asks teachers what makes them intervene by
selecting one or a set of indicators in order to allow
the detection of their teaching intentions by the
system. To validate our proposition of detecting
teaching intentions in a declarative way, we made a
pilot test in which we used a TEL system: Hop3x is
described in the next section.
3 HOP3X: THE DESIGNED TEL
SYSTEM
In this paper, we want to infer teaching intentions
from analysis of indicators. To tackle this issue, we
used a TEL system named Hop3x. This TEL system
was designed for learning programming. In our
work, we use it in object-oriented programming.
Hop3x is a track-based TEL system and three
applications compose it:
Hop3x-Student allows learners to edit, compile and
run code and program. It also allows them to
call for help when needed via a communication
tool.
Hop3x-Server collects learners’ interaction tracks
and saves them as Hop3x events. It allows
real-time calculation of indicators.
Hop3x-Teacher is a follow-up and intervention tool
for teachers. It allows them to manage a group
of learners in a situation of distance and
real-time lab work (Fig. 2), It also allows them
to follow learners’ activities in real time thanks
to a visualization interface, to have synthetic
information about learners’ productions and
tasks through indicators, to annotate a part of
learners’ program and make them see this
annotation, to intervene via communication
tools, to replay learners’ trails during or after
SUPPORTING THE IDENTFICATION OF TEACHERS' INTENTION THROUGH INDICATORS
113
the session, to visualize the history of their
interventions via a reminder module.
Figure 2: Snapshot of a teacher’s interface using Hop3x.
Learners’ supervision in real time is based on the
architecture of Hop3x. Hop3x architecture based on
client-server model, allows a real-time track of
learners’ interaction as events. An event stored in
Hop3x can be a project creation or removal, a file
creation or removal, a program compilation, a
program run, a text insertion or deletion, etc.
Based on this real time track of learners’
interaction Hop3x performs (a) teachers’ real-time
follow up of learners’ activities, (b) calculation of
indicators and (c) teachers’ visualization of these
indicators related to learners’ tasks progress and
trails.
4 PILOT TEST
The pilot test dealt with an academic French
university context. It was carried out from January
2010 to March 2010 and took place with two
teachers and thirty-six learners split into two groups
of eighteen. Each group was working on three
topics, at the rate of on topic per three-hour lab work
session.
The lab work sessions were part of a course
entitled “Object-oriented programming and Java”.
The learners involved in our pilot test were
undergraduate students. These learners were novices
in Java programming but during the preceding term,
they were introduced to the basics of object-oriented
programming. Before each learning session using
Hop3x in which learners practiced Java
programming, they attended lectures and tutorials
about the notions and concepts that they would
implement during lab work.
During a learning session (lab work), there was
no face-to-face interaction between teachers and
learners. Teachers could, in real time, follow
learners’ tasks and activities i.e. learners’ programs
and java codes and could interact with learners
through communication tools by selecting a set of
indicators (declaring their intentions) and by
choosing the way (talking or sending a message)
they intervene.
Teachers could be proactive or reactive. Indeed,
teachers could intervene, either because learners
directly solicit (reactive modality) or on they own
initiative owing to indicator values therefore
(proactive modality).
Three tutoring tasks that teachers have to
perform during lab work were identified:
(a) Managing the progress of learners’ activities
depending on the time.
(b) Supporting learners in their knowledge and
skill acquisition
(c) Coaching learners in their acquisition of good
programming practices.
For each tutoring task indicators allow teachers
to make decisions. We identify two kinds of
indicators that reflect both quantitative and
qualitative aspects of learners’ activities: (1) specific
indicators are linked to one topic or subject of lab
work, e.g. there are indicator about the mastery of
the encapsulation concept, (2) transverse indicators
are not linked to one subject of lab work, e.g.
indicators about the use of appropriate programming
style (respect of javaStyle rules), the writing of
comments (especially javaDoc comments).
This pilot test fed our corpus and allowed us to a
large amount of tracks and events. On average, we
obtained 3995 events per learner for three hours of
lab work that is 386 312 events in total, on which
our results – which will be presented in the next
section – are based.
5 EXPERIMENTAL RESULTS
One of our issues, when we wanted to identify
teaching intentions in a declarative way was the
possibility that this declarative approach could be
constraining for them and thus they could refuse to
put this information in the system because it could
overload them with an additional task.
To get those results, we analyzed the collected
tracks from the pilot test, especially the
CSEDU 2011 - 3rd International Conference on Computer Supported Education
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interventions. For each intervention, we checked if
teachers declare their intentions by selecting
indicators. We dealt with 242 interventions that
teachers made during the experiment (interventions
for all groups and all lab work). Out of these
interventions, 29, i.e. 11%, are reactive i.e.
interventions caused by a learner’s call for help. 213
(about 89%) are proactive. We are interested in
proactive interventions caused by indicator values.
Most of the time, in these proactive
interventions, teachers selected indicators while
intervening: in 175 interventions (82%), teachers
declared their intentions.
Out of the remaining 38 interventions (18%),
half (18) were interventions in which teachers
wanted to select indicators but had not defined them
before the learning session; thus, the system could
not calculate them. In the last portion which includes
18 interventions (9% of all proactive interventions),
teachers forgot to select indicators while intervening,
although the latter were available.
The second issue of our work, when identifying
teachers’ intention was the possible
non-correspondence between what teachers declared
they wanted to do and what they had actually done.
To address this issue, we obtained some figures
from the analysis of 175 interventions, those in
which teachers had selected indicators. To check the
correspondence between the contents of teachers’
interventions and the indicators they selected, we
listened to 93 audio interventions that had been
recorded during the pilot test. We also scrutinized 82
textual interventions extracted from the corpus of
tracks we collected.
Tab. 2 shows the results of the analysis and
presents the relationship between teachers’
intervention contents and the problems pointed out
by the indicators they selected. As we can see, the
contents of teachers’ interventions do not match the
indicators they selected in only 3,6% of cases.
In about 60%, the contents perfectly match the
problems pointed out by the indicators selected by
teachers during their interventions. The remaining
36.6% (i.e. 21 interventions out of 175) corresponds
to a partial correspondence. In this category, in 90%
of cases correspond to situations in which teachers
went further in their interventions than they had
declared: they solved more problems than they had
declared through the choice of indicators. By
contrast, in the remaining 10%, intervening teachers
didn’t deal with all the problems they had declare to
solve.
Table 2: Relationship between teachers’ interventions
contents and the indicators they selected.
Correspondance
No
correspondance
Partial
correspondance
Topic 1 58% 2% 40%
Topic 2 65% 3% 32%
Topic 3 56% 6% 39%
Total 59,66% 3,66% 36,6%
6 DISCUSSION
Getting and identifying teachers’ intentions during
learning sessions is not easy because of the way we
choose to get this information. This identification
can be disrupted or can even fail in two ways. First
the declarative way adds another task for teachers
during learning sessions; it can thus overload them
with work and be constraining for them. Secondly,
what teachers really do in their interventions does
not always match what they declare to do.
Thanks to the pilot test, we could implement our
approach and assess if this way of getting teaching
intention is effective. Results of the pilot test reveals
that most of the time teachers declare correctly their
teaching intentions trough indicators.
In fact, we are interested in proactive
interventions because in reactive ones, teachers react
to learners’ call for help. Indeed, there are no
indicators to select to identify their intentions since
they want to support learners for problems of which
they have no prior knowledge at the time of the
intervention.
In taking into account these proactive
interventions (about 90% of 242 interventions, i.e.
213 interventions), the analysis of the data tracks
from the pilot test and related to our initial issues
shows various elements:
(a) In 82% of their interventions teachers
correctly put the information about their teaching
intention into the system. It seems that this way of
getting teaching intentions is not constraining for
teachers because the percentage in which they did
not give them is very low (18 interventions out of
213, i.e. 9% of proactive interventions).
(b) Among these 82% of cases (i.e. 175
interventions) in which teachers declare their
intentions, the percentage of interventions in which
the selected indicators had no relation with the
interventions contents is very low: it represents only
3.66% of 175 interventions. The number of cases in
which there is a perfect correspondence between the
intervention contents and the problems underlined
by the indicators that teachers selected, is acceptable
SUPPORTING THE IDENTFICATION OF TEACHERS' INTENTION THROUGH INDICATORS
115
because it represents 105 interventions (out of 175,
i.e. about 60%).
The case of partial correspondence represents 64
interventions (out of 175, i.e. 36.6). In 4 of these,
teachers selected too many indicators while
intervening. We noticed while listening to the audio
conversations that these interventions lasted more
than 5 minutes. In analyzing the contents, we also
learned that in these cases teachers dealt with one
problem and interacted with the learner about it but
the latter had difficulty resolving the problem.
Teachers took time to explain, step by step, how to
come to a resolution of the problem. Thus, we can
suppose that in these situations they did not want to
overload the learner in giving him a lot of
information. They tried consequently to help
learners gradually by first resolving the problem
with which they had difficulties, and then took the
remaining ones into account.
In the case of partial correspondence, 60
interventions concern situations where teachers do
more than they had declared. They have done the job
in so far as they have actually interacted with the
learner about the selected problems. Moreover,
doing more than was originally declared poses
problems because the identification of teaching
intention is not complete. Thus, since teaching
intention is at the core of teachers’ self-regulation,
its incomplete identification can bring up some
issues at the time of the instrumentation of the
self-regulation process.
7 CONCLUSION AND OUTLOOK
In our work, we want to instrument teachers’
activities during learning session. Here, we focus on
identifying teachers’ intentions through a declarative
approach by asking teachers what makes them
intervene. For that, we offer them a tool in which
they can select a set of indicators, which are
supposed to be the triggers of their interventions.
Experimental results show that most of the time (in
82% of interventions) when they intervened teachers
declared their intentions through indicators
selection. However, partial correspondence between
the interventions contents and the problems
underlined by indicators teachers selected while
intervening arise new issues. Indeed, incomplete
identification of teaching intentions could lead to
failure or errors during teachers’ self-regulation
support since this latter is based on teaching
intentions. Addressing these issues will be our
short-term objectives by giving teachers the
opportunity to adjust their intentions after the
interventions (add or deletion of some indicators
from the list of indicators selected pre-intervention).
Our mid-term objectives will be the implementation
of teachers’ self-regulation process and its
evaluation by carrying out a new experimentation.
We also plan for our long-term objectives to propose
learners some of the indicators available for teachers
in order to support self-regulated learning (Butler et
al., 1995).
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