MODELING USER PROGRESS AND VISUALIZING FEEDBACK

The Case of ProPer

Ioannis Kazanidis and Maya Satratzemi

Department of Applied Informatics, University of Macedonia, 54006, Thessaloniki, Greece

Keywords: User Feedback, Progress Visualization, AEHS, Motivation.

Abstract: In traditional classroom education instructors can detect the learning difficulties of their students and take

all the necessary actions in order for the problem to be overcome. In asynchronous online education,

however, it is system functionality that plays a vital role in locating potential problems that may be related

to either or both the students and the courses. In addition, students expect the online course to provide them

with personalized instruction and useful feedback. This paper presents how ProPer, an adaptive SCORM

compliant Learning Management System, monitors and visualizes user progress through instruction in order

to help teachers and students alike to locate possible weaknesses. Moreover, ProPer, through immediate

feedback and comparative techniques aims to motivate students to continue with their study. Evaluation

findings indicate that ProPer can help both instructors and students with their work through out the learning

process.

1 INTRODUCTION

Adaptive Educational Hypermedia Systems

(AEHSs) aim to individualize the learning process to

meet students’ characteristics, preferences and goals.

These systems through the Adaptive Navigation and

Adaptive Presentation techniques provide

personalized courses over the Internet so as to help

distant learners improve their learning outcome.

A new research stream in adaptive systems is

attempting to integrate several tools from Learning

Managements Systems (LMSs) in order to provide

as many teacher and learner support features as

possible as well as enable teachers to easily

administrate their courses and classes. The goals of

this stream is on the one hand to provide the teacher

with tools that will help them to easily develop and

manage online courses and virtual classrooms, while

on the other support students with discussion

forums, quizzes etc., as well as providing

adaptivitity. The development of these systems,

however, is not based on any common framework,

as there are no fixed rules, techniques or methods

acceptable to all. Consequently, although developers

spend a lot of valuable time, money and effort on

their applications, they pay nowhere near enough

attention to instructional strategies. Moreover, due to

the absence of a common framework, the courses

along with the educational content produced are

deficient in reusability, interoperability and

durability. More specifically, it is difficult to apply

the educational content from one course to another;

or to distribute a course from one AEHS to another,

since most times they are not compatible. A

proposed solution to this problem is the adoption of

recently accepted educational standards like

SCORM (Sharable Content Object Reference

Model) in order to provide RAID (Reusable,

Accessible, Interoperable, Durable) courses.

In previous works(Kazanidis and Satratzemi,

2009a;2008) we combined the basic functions of an

LMS with the adaptive features of AEHSs and the

adoption of the SCORM standard and its

specifications. Furthermore, for better learning

outcomes, we promoted the concept that it is

possible to adapt native SCORM compliant courses

to user learning style (Kazanidis and Satratzemi,

2009b).

Even if the above functionality enables

instructors to easily create and manage online

courses, and support students by providing

personalized instruction, another problem arises due

to the nature of asynchronous online education.

Namely, web courses can be used either as an

independent integrated solution for distance learning

or as supplementary material for conventional

Kazanidis I. and Satratzemi M. (2010).

MODELING USER PROGRESS AND VISUALIZING FEEDBACK - The Case of ProPer.

In Proceedings of the 2nd International Conference on Computer Supported Education, pages 46-53

 SciTePress

classroom education. Therefore, while teachers in

traditional classroom education can detect problems

and possible student weaknesses, those studying

through distant online education can depend only on

the system’s ability to detect these weaknesses and

to reform user instruction accordingly, whereas in

the case of supplementary instruction it would be

better for teachers to monitor user progress and

attitudes so as to spot possible learning difficulties.

Regrettably, LMSs do not adapt their instruction

according to user needs and even worse they do not

provide enough information to the teacher to enable

them to spot student weaknesses. On the other hand,

AEHSs are attempting to adapt their instruction in

numerous ways (providing further explanations,

more examples – activities, proposing concepts for

study etc.) however; they too do not provide

sufficient information to either teacher or user in

order to ascertain user weaknesses and progress.

We believe that distant education systems have

to observe user interaction with the system and draw

conclusions about both user and course status

providing instant feedback not only to teachers but

also to learners. Feedback about course status will

motivate students to continue with their study and at

the same time inform them about any possible

weaknesses they may have, while instructors will be

able to monitor user progress and identify potential

problems as well as detect possible course

shortcomings.

This paper presents how ProPer (Kazanidis and

Satratzemi, 2009a;2008) an adaptive SCORM

compliant LMS visualizes user progress through

instruction in order to help both teachers and

students locate possible weaknesses. In addition, by

providing immediate feedback and comparative

techniques, ProPer aims to motivate students to

continue with their study.

2 RELATED WORK

In order for AEHSs to be able to be adapted to users,

they have to be aware of the user’s initial and

current knowledge of the domain, as well as his/her

individual characteristics. This information is stored

in the User Model (UM) of adaptive systems. All

this data about the user can be divided into two main

categories (Kavcic, 2000). One category contains the

information about user knowledge of the domain and

the other all the information that is independent of

the domain such, as the user’s individual

characteristics, preferences, learning style etc. This

data can be either static, when gathered at the

initialization of the user model and does not change

during the educational process (user individual

characteristics, preferences, capabilities etc.) or

dynamic, when it is collected throughout the

learning process (user progress, current knowledge,

actions, study time etc.) (Kavcic, 2000; Carmona

and Cionejo, 2004). As a rule, students’ personal

characteristics are static data while their knowledge

is dynamic.

It is clear from the above that the UM can track a

lot of useful information about user progress and

attitudes. The challenge at this stage lies on three

axes a) to provide appropriate adaptivity for

personalized instruction; b) to provide instant

feedback; and c) to allow teachers to monitor user

progress and attitudes.

As this work deals with visualization of user

progress, a short presentation of the visualization

techniques used in adaptive systems will be made.

Most of the AEHSs only give emphasis to the first

of the three axes mentioned above. The visualization

of user progress, most times comes with adaptive

link annotation in the course structure view. More

specifically, in order to present whether a concept is

considered as known or not AES-CS (Triantafillou

et al., 2003) applies a checkmark after the link of

each course concept. A checkmark is also used in

Interbook (Brusilovsky et al., 1998) where no

annotation signifies that the concept is “unknown”, a

small, medium or big checkmark means “known”,

“learned”, “well learned” respectively. Likewise,

INSPIRE (Papanikolaou et al., 2003), applies a

checkmark to indicate whether a concept is

considered as known. In addition, in order to

visualize user progress according to course

outcomes, INSPIRE uses a glass to show user

knowledge. The amount of user knowledge is related

to how full the glass is. Similarly, ELM-ART

(Weber and Specht, 1997) employs an extension of

the traffic light metaphor to annotate links visually.

A yellow sphere before the link indicates that the

corresponding concept is considered as being known

or having been visited and in the case of it being a

test or a problem that it has been solved correctly.

Anes (Kavcik, 1999) is yet another system which

annotates concept links using specific colors for

each state.

Most of these adaptive systems, however, do not

provide comprehensive feedback to the user -or

instructor- about their current course status,

encompassing coverage of course concepts, user

goals, study time, number of visits for each concept

etc.What is more, the teacher is not able to identify

neither student nor course weaknesses so as to take

MODELING USER PROGRESS AND VISUALIZING FEEDBACK - The Case of ProPer

appropriate action. Even the majority of LMSs, such

as Moodle, Claroline etc. do not provide a statistics

module, which enables the instructor to obtain

specific information about the performance of each

student (Romero et al., 2007).

Other researches (Ortigosa and Carro, 2002) aim

to support teachers in detecting the need to modify a

course when it does not fully meet every student’s

requirements. More specifically, an agent has been

developed that, among others, identifies users with

the best/worst results and the practical tasks where a

significant number of users have obtained non-

satisfactory results. It also detects points where a

considerable number of users revisit previous

sections and makes conclusions about the need for

improving the content. CourseVis (Mazza and

Dimitrova, 2004) is a visualization tool that tracks

web log data from a Content Management System.

By transforming this data, it generates graphical

representations that keep instructors well-informed

about what precisely is happening in distance

learning classes. More specifically, it employs

information visualization techniques to produce

graphical representations of learners’ social,

cognitive and behavioral aspects allowing instructors

to identify certain tendencies that may exist in their

classes, or to quickly identify individuals that need

special attention. Furthermore, other tools, such as

Logic-ITA (Merceron and Yacef, 2003) attempt to

extend web-based tutoring systems that make

instructors aware of their class progress as well as

any problems that may be encountered.

These systems, however useful they may be for

teachers, do not appear to benefit students in any

way since most times their results are not available

to learners. A system that opens the student model to

the user is KERMIT (Hartley and Mitrovic, 2002),

which among others shows a hierarchical

representation of student’s progress with the ability

to expand some of the subcategories of the course.

As far as we are aware, none of the above

systems accounts for learners’ progress in

accordance with the student’s educational goals

since either they provide information at the concept

level or measure user progress over the whole course

as an integrated domain. Therefore, in the case

where a learner wants to study only a part of the

domain, the results may be inaccurate. We promote

the development of tools that can determine a

learner’s progress both to his/her educational goals

as well as to the whole course. In the following

sections, we present how ProPer assists both

students and teachers by tracking the appropriate

data and sending them feedback about student and

course status.

3 FEEDBACK VISUALIZATION

IN ProPer

ProPer is an integrated adaptive and adaptable

learning environment that offers LMS administration

facilities and conforms to SCORM specifications. It

comprises a combination of an AEHS and a LMS,

while providing a variety of additional educational

features on the one hand for the learner (Instant

feedback, dynamic F.A.Q., Draft notes, Java Online

Editor-Compiler etc.) and on the other for the

teacher (course-user statistics, course management

etc.). It therefore takes advantage of the strengths of

both system categories. The system can be adapted

to the learner’s progress, previous knowledge, goals,

navigation through the course, as well as his/her

learning style. Even if ProPer uses most of the

adaptation techniques as identified by Brusilovsky

(2001) we wanted to not only support students with

their study by providing personalized instruction but

also to try to motivate them for a better learning

outcome. Research (Hartley and Mitrovic, 2002) has

shown that feedback about user progress can

motivate students to achieve better outcome results.

Thus, we decided to provide students with the

appropriate feedback about their progress on the

course. In addition, many research streams like those

referred to in related works, aim to inform

instructors on class progress and any potential

problems that particular students or even specific

parts of the course may be experiencing. This will

help authors to support weaker students and to revise

their courses so as to rectify any problems. For these

reasons ProPer provides instructors with detailed

statistics both for user and course status.

3.1 Design Guidelines

Some basic guidelines where followed in order to

visualize feedback about user and course status:

1. Simple and Easy to use Feedback. Many non-

technical instructors encounter problems

administering their courses and even worst,

students without a technical background may

have a cognitive overload problem in the case of

either a complicated interface or system

functionality. Thus, ProPer was designed for use

by non-technical instructors and students,

providing simple and easy to understand

feedback.

CSEDU 2010 - 2nd International Conference on Computer Supported Education

Figure 1: System Architecture.

2. Available Feedback to the Learners. Since

feedback can motivate learners with their study,

it is made available to them throughout the

learning process.

3. Real-time Feedback of Learner’s Progress

and Detailed Feedback through Specific

Tools. Simple feedback about user progress is

available in real-time while an option for more

detailed feedback will enable students to check

their current course progress in more depth.

4. Detailed Feedback about Course and

Learners’ Status to Instructors. The system

provides instructors with detailed statistics for

every course, who also have the option of

checking the entire class progress of the course

as well as each individual user’s progress and

attitudes.

5. Allows Learners to send their own Feedback

to Instructors. In addition to any likely

problems that the course might have, learners

may encounter some difficulties with specific

course concepts. In both cases, the system

provides the necessary mechanism to enable

learners to easily send feedback to their

instructors about every single course concept.

3.2 System Architecture

The system has a combined architecture of SCORM

LMS and AEHS. As shown in simplified form in

Fig. 1, we adopt the typical SCORM Run Time

Environment (RTE) structure adding an Adaptation

Module (AM) and extending the pre-existent

Domain and User Models. Therefore, the system

consists of: i) the Domain Model (DM) that

represents the system’s domain knowledge; ii) the

User Model (UM) that represents the particular

user’s knowledge of the domain as well as his/her

individual characteristics (both these models

comprise the system’s Data Model (DtM)), iii) the

AM which interacts with the DtM and the UM in

order to provide adaptive navigation to the course;

and iv) the RTE Sequencer that is triggered by

course navigating controls, interacts with the DM

and delivers the appropriate educational content to

the learner.

To be able, however, to provide feedback to

users and instructors, ProPer needed two additional

mechanisms. Firstly it had to be able to track user

progress and interaction with the system and

secondly, it had to implement the necessary tools to

calculate the appropriate feedback and visualize it in

an easy, useful and understandable way. Therefore,

two new modules were integrated, the User Tracker

(UT) and the Feedback Visualizer (FV) (Fig.1).

The UT monitors learner’s interaction with the

system and stores all the essential data into the UM.

When feedback has to be delivered, the FV initially

calculates the information that has to be sent, then it

visualizes results and delivers them to the user.

3.3 Modeling Cognitive Aspects

An initial survey with instructors revealed that they

were concerned as to how they could supervise a

virtual classroom through an LMS and how

students’ learning difficulties can be recognized in

order for the appropriate actions to be taken. They

also drew attention to the importance of a tool that

gives them feedback on their already developed

MODELING USER PROGRESS AND VISUALIZING FEEDBACK - The Case of ProPer

courses facilitating improvement.

Based on the above, we thus, decided to track the

following user data:

 A score for every course concept. This is the

score that a user gets on completion of their

study of a specific course concept, which shows

the level of knowledge acquired for each

concept;

 The number of visits to a concept. The user may

only need to study a course concept once or may

need to go back and study it again in order to

reinforce learning. This number will help both

learners and instructors to pinpoint possible

difficulties and problems in specific parts of the

course;

 Study time. The time that a student spends on a

course indicates how easy or difficult it is for

them to learn the course domain.

 Based on the above data some additional useful

results can be calculated, such as:

 Course score – user progress. This score can

show the level of knowledge acquired on the

domain of the course. It actually shows user

progress through the learning process.

 Coverage of educational goals. Often on web

courses users have different needs and therefore

different educational goals, for this reason,

ProPer allows learners to define which parts of

the course they wish or not. For those learners

who want to study particular parts of a course, it

is more relevant that the feedback provided is

related to their own educational goals rather than

to the whole course. Thus, coverage of

educational goals is the percentage of the

learner’s educational goals learned so far.

 Position in the class. This number will show the

student their class position in comparison with

their fellow students on the course. We have

strong reason to believe according to our survey

(Lawrence et al., 2004; Regueraset al., 2008) that

this helps to motivate students to study harder

with a view to improving their score position, i.e.

it stimulates healthy competition among learners.

A special mechanism was incorporated to

calculate the user’s score on both the course and

goals covered. ProPer courses are compliant with

SCORM and thus have a tree structure consisting of

leaf as well as parent activities. Leaf activities can

be either Sharable Content Objects (SCOs) or

Assets. SCOs are the smallest logical entities in a

course that can communicate with the system and

generally they denote a course concept, while assets

are elementary pieces of knowledge, such as text,

images, video etc. A parent activity is one that

contains other parent or leaf activities that are

referred to as its children. Lastly, it should be

explained that each activity has a weight on their

parent’s score.

In order, therefore, to ascertain user progress,

initially the score of every concept is retrieved as

well as its weight on its parent’s score. In this way,

the system calculates every parent’s score, until it

reaches the primary parent of all the course’s

activities. The score of the primary parent is the one

that the user has in regards to the whole course.

More specifically, let us assume that LA is a leaf

activity, PA a parent activity which contains one or

more child activities. CA is a child activity where

CA  {PA, LA} and n is the number of CAs

included in a PA. Every activity has a score  [-1, 1]

and a weight  [0, 1]. These values are retrieved

from the system’s data server and only the PA scores

have to be calculated according to their CA weights

and scores. Now, let us assume that CA

is the i child

of a PA. Also PAscore is the score of a PA;

CAscore

, CAweight

are respectively the score and

the weight of CA

. Therefore, the following formula

is used to find the score of a PA:









CAweight

CAweightCAscore

PAscore

(1)

A more complicated procedure is, however,

followed for the calculation of goals covered. Let us

assume that goalscore and goalweight are the goal

score and goal weight of an activity. Therefore,

LAgoalscore and LAgoalweight are the score and

weight of an LA according to user educational goals,

while LAscore and LAweight are the score and

weight of an LA in regards to the whole course.

Similarly, PAgoalweight and CAgoalweight

are the

goal weights of a PA and a CA

. In addition,

PAweight and CAweight

are the weights of a PA and

the CA

of the PA as defined by the course author. In

order to find the user score in accordance with the

student’s educational goals we need to first find the

goal weight of each activity. Thus, the following

rules apply:

IF LA is a goal THEN LAgoalweight =

LAweight ELSE LAgoalweight = 0

(2)

IF PA is a goal THEN

CSEDU 2010 - 2nd International Conference on Computer Supported Education

(b)

(c)

(a)

Figure 2: Screenshots of student feedback.











CAweight

htCAgoalweigPAweight

htPAgoalweig

ELSE PAgoalweight = 0

(3)

Following the calculation of the goalweight for

each activity, the goalscore of each parent activity

must also be found until the primary parent of the

course is reached. Let us further assume that

PAgoalscore and CAgoalscore

are the goal scores of

the PA and the CA

respectively. In order to

determine the goalscore of a PA the following

formula is used:











htCAgoalweig

htCAgoalweigeCAgoalscor

ePAgoalscor

(4)

3.4 Visualizing Cognitive Aspects

Feedback in ProPer is offered in two ways: i) that

which is provided to the students and whose

principal aim is to motivate them with their

studying; and ii) that which is provided to the

instructors and whose chief aim is to enable the

detection of learning difficulties that their students

may have or develop, and to detect potential

problems the course may contain.

3.4.1 Student Feedback

It was decided that learners would receive feedback

in three different ways. The first is at the selection of

a course where they can view both their score and

their position in the class in comparison to the other

students on the course (Fig. 2a). The second is

throughout the duration of the course. Learners have

access to their scores on the basis of the whole

course as well as their individual educational goals.

These scores are demonstrated as a percentage as

well as a colored bar (Fig. 2b). In addition, when a

user places the mouse over a course activity –

concept in the table of contents, then the score of

this activity is given. Moreover, all learners can at

anytime select the feedback button on the top right

of the screen (Fig. 2b) and send instant feedback to

their instructor related to the specific concept that

he/she is studying. In this way, the instructor can be

acquainted with learners’ opinions about the course.

The third way for the students to obtain feedback is

with a screen that presents detailed statistics about

the course (Fig. 2c). On this screen they can see their

course score, the goals covered, their position in

comparison to the other users, as well as the score,

time spent, and number of visits for every activity.

Furthermore, goal activities are marked with a red

target icon, while pre-known activities and those

known through study are annotated with a man and

an open book respectively with a checkmark on top.

This screen enables learners to view their exact

status in a course and to further define their actions.

MODELING USER PROGRESS AND VISUALIZING FEEDBACK - The Case of ProPer

Figure 3: Screenshot of instructors’ feedback for course status.

3.4.2 Instructor Feedback

The system also sends feedback to instructors not

only on every course but also on the status of every

student. More specifically, if an instructor chooses to

see feedback on a specific student, he/she initially

sees a screen similar to Fig. 2a displaying the

course’s name that the student has registered for,

followed by the student’s score and position. Then,

the instructor may select one of the courses and see

further details as presented in Fig. 2c. On this screen

they can figure out the real user status about the

course. In the case where an instructor wants to see

feedback on one of their courses, then a two-part

information screen is displayed (Fig. 3). The first

shows the users registered for that course as well as

their scores and their study times, allowing the

instructor to select one user to view further details.

The second part shows the mean score and mean

study time that users spent studying the course as

well as the number of registered users. Next, the

system provides the following details for every leaf

activity of the course: i) mean score, ii) the

percentage of students that knew the corresponding

concept before their study, iii) mean study time, iv)

the percentage of the students who consider this

activity as one of their goals, v) the number of

students that studied the activity, vi) the total

number of visits, and vii) whether student feedback

is available. If feedback is available instructors may

select the related icon and read it. It is worth

mentioning that these results can be exported to an

excel file for further statistical analysis. In this way

instructors can decide on the courses’ usefulness or

about the possible problems that learners may come

up against.

4 CONCLUSIONS

In this paper we have presented how ProPer, a

SCORM compliant LMS with adaptive features

provides and visualizes feedback both to learners

and instructors. Despite the fact that many systems

do not open the user model to learners, ProPer does

just this and in so doing provides them with detailed

feedback about their progress on the learning

process. In addition, most of the SCORM compliant

or AEHSs do not provide detailed feedback at either

the course or concept levels. Perhaps worse is the

fact that the feedback they do send is related to the

course rather than the individual’s educational goals.

In contrast, ProPer calculates and delivers feedback

according to both the course domain and to specific

learner goals. This feedback can be provided at

either the course or concept level. Furthermore, even

if some LMSs provide some kind of feedback to

instructors, they in no way incorporate such detailed

feedback as ProPer does.

We believe that the kind of feedback presented

above will, on the one hand greatly benefit learners

CSEDU 2010 - 2nd International Conference on Computer Supported Education

by motivating them with their study and making

them aware of their weaknesses, and on the other,

ProPer’s functionality will facilitate instructors to

supervise a virtual class and inspect their courses for

potential problems and/or omissions.

A formative evaluation of ProPer (Kazanidis and

Satratzemi, 2009a) has shown that instructors had a

high perception of the system’s usefulness. They

stated that they found the screens displaying

feedback on user and course statistics as one of the

most valuable system features. One of their

suggestions, incorporated into the revised system,

was for functionality to be added to enable every

user to send direct feedback to their instructor on

course activities. The student’s evaluation of the

system’s functionality gave mixed views, namely

that while some liked the feature very much that

shows the user’s score and position on the course in

comparison to the rest of the class, there were those

who felt this could be counter-effective in regards to

motivation. We believe that this reflects the

individual’s learning style, some people like and

thrive on competition, while others do not. We are

therefore considering offering users the option of

choosing their learning style when they sign up in

the system and later allowing the system to

dynamically decide on the appropriate functionality

for each user.

In concluding it can be said that the same

framework as that applied for ProPer can be

implemented by every SCORM compliant LMS and

many AEHSs, so as to improve learners’ motivation

and simultaneously increase the usefulness for

instructors.

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