Collaborative learning: Reasons that influence the participation of students in distance education fora

Hellenic Open University,

School of Sciences & Technology,

Kiriakos Patriarcheas - Michalis Xenos

A key tool that supports communication in distance education is the electronic forum or e-forum.

e-forumDistanec education

In recent years, the Hellenic Open University (HOU) has turned to the modeling of messages in order to classify the interventions of participants in its fora into large categories in order to detect where the subject of interest of the discussion is focused.

Content

Goal, When, For who, Where

Data

Method, Modelling in formal Language

Tool

Data analysis

Questions

Goal

This study focuses in the study of the reasons that influence the participation of students in a forum, by studying the causes that strengthen or discourage participation in the HOU fora.

When

For the academic years 2005-8

2005

2008

2006

2006 2007

2007

For who Within the framework of a course module

(INF10) of School of Sciences & Technology

WhereIn Patras, Greece.

Data

• The data comprised of 423 discussion threads

with 3,542 messages and 6,694 message

content categories.

Method

This study uses a specific modelling developed for Hellenic Open University’s fora

Modelling in formal Language

There are two categories of communication’s carriers: a) Teachers, b) Students (For brevity reasons, teachers shall be symbolized with T and students with E)

As for the type of message, they are discerned to questions and replies (answers). Using the symbols q and a respectively.

As for their content category, we use the symbols: M, X, P, I, F, D, J, G, V, L

The order in which appear the above symbols is: a) the message carrier, b) the type of message and c) the content category to which the message belongs.

Content categories

i) study of educational material (M), ii) questions/answers for exercises – assignments (X), iii) presentation of sample assignments by tutors (P), iv) instructions (I), v) assignment comments, corrections (F), vi) student comments on assignments (D), vii) sending – receiving assignments (J), viii) sending - receiving grade marks (G), ix) notification of advisory meeting (V) and x) pointless message (L).

Rules The grammar P: A set of rules of the form α → β, where α

and β sequences containing terminal and non-terminal symbols and α is not an empty sequence, as follows:

(1) S → ruS (8) y → q (15) c → F

(2) S → ε (9) y → a (16) c → D

(3) u → uyc (10) y → ε (17) c → J

(4) u → ε (11) c → Μ (18) c → G

(5) r → T (12) c → X (19) c → V

(6) r → E (13) c → P (20) c → L

(7) r → ε (14) c → I (21) c → ε

An example Sequence EqMEqXTaMX : Ε for the student’s capacity, q for the question, Μ as it concerns the

study of the educational material, Χ for the fact that the next message concerned an assignment, T for the teacher’s capacity, a for the fact that it is an answer, M for the fact that this reply concerns the study of educational material and X for the fact that the second part of the message concerns an assignment. According to the above, the sequence EqMEqXTaMX constitutes a sentence of the Language because:

Rule: (1) (1) (1) (3) S —>ruS —>ruruS —>rururuS —>ruycruycruycS

(4)(6)(8)(11) (4)(6)(8)(11) —————> EqMruycruycS —————> EqMEqXruycS

(3) (2)(4)(5)(9)(10)(12) —>EqMEqXruycycS ————————> EqMEqXTaMX

The Tool

According to this approach, it was developed a

system of automatic classification, which

comprised the following: a) Data filtering: b) Storage of roots files: c) Strings’ production:

Data filtering

Where there are considered as input some web pages accommodating the discussion threads of a distance education forum of HOU (which include much data having no essential information concerning the educational procedure e.g. titles, images etc.) and creates a temporary file with the “useful” part (User name, date, message’s content) which may become a source of information for educational conclusions.

Storage of roots files A dynamic way according to which word or phrases or

symbols roots are stored, as well as the respective terminal symbols q if it is a question or a if it is an answer. The same thing was done also for the storage of information necessary for the determination of content category of a message, i.e. if it is about study, assignment, comment etc. or combination of them (e.g. a message concerning both the study and an assignment). To wit, it takes as input couples of information of the type root of a word or phrase and terminal symbol of the content category (M, X, P, I, F, D, J, G, V, L). The system provides the ability to add further content categories if necessary.

Strings’ production

Receiving as input the temporary file with the “useful” information (User name, date, message’s content) and the files with the couples of roots words/ phrases/ symbols and terminal symbols and presents (and stores) the respective strings with the relative extensible file, so as the results to be kept for further exploitation.

E q M E q X T a M X

Input

Output

Representation of discussion thread in simple string

Output after the addition of User names and dates.

Data analysis Based on the above methodology, if for each discussion thread

we take into account who starts it (Tutor or Student) then it is apparent that the threads started by a tutor have more messages: 10.97 messages/thread versus 5.06 in threads started by students.

TABLE IThe ratio of messages per discussion thread in INF 10 of HOU during

years 2005-8

Threads that initiated by the students

Threads Messages Messages/Threads

186 941 5.06

Threads that initiated by the tutors 

Threads Messages Messages/Threads

237 260110.97

 

It should be noted however that this phenomenon does not have the same intensity

throughout the academic year, but there is a rising trend in the months October

through December, a fact that means the gradually increasing participation of students

in the forum in the first months of the academic year, followed (in January) by a decline

of the effect of the phenomenon, a sharp rise in February and then an ongoing decline

until the end of the academic year.

Data analysis

Data analysisTABLE II

The ratio of messages/discussion thread in total (A) and in threads started by the Tutor (B) per month

MonthTotal Messages/

Threads (A)

Messages/Threads begins from the Tutor

(B)B /A

O 9.69 12.37 1.28

N 9.28 13.73 1.48

D 9.48 11.24 1.19

J 7.45 9.82 1.32

F 7.52 10.33 1.37

M 7.77 10.00 1.29

A 7.17 7.54 1.05

M 7.23 8.55 1.18

J 9.13 8.53 0.93

J 4.70 6.67 1.42

A 1.43 3.00 2.10

S 3.11 5.67 1.82

Data analysis

The period when a discussion thread is started plays a definite role, and we can distinguish 4 distinct periods: a) high participation in the first active months (October-December), peaking in November, b) followed by a period of decline (January-March), with a peak in February and c) lower participation period (April - May), with the threads started by the tutor always having preponderance over the total number and d) very low participation (June - September), with the exception of June, something which is mostly due to the fact that exam results are announced and explained by the tutor

and the students have a relevant discussion.

Data analysis

We should also take into account in the above that in the months

November and February the 2 first written assignments are submitted,

a fact that explains (proportionally) the two peaks of participation.

Data analysis

Data analysisTABLE III

Number of discussion threads and messages in total and in threads started by the Tutor per month

Month

Threads Messages

Totalbegins from

TutorTotal

in threads that begins from Tutor

O 91 54 882 668

N 80 48 742 659

D 63 41 597 461

J 22 11 164 108

F 29 15 218 155

M 26 13 202 130

A 24 13 172 98

M 22 11 159 94

J 30 17 274 145

J 20 9 94 60

A 7 2 10 6

S 9 3 28 17

With regard to which subject categories are the focus of the discussion, based

on this methodology, it arises that categories questions/answers for exercises -

assignments (X) and study of educational material (M) are the most popular.

Data analysis

An important category also is student comments on assignments (D) which comes in 3rd totally with 919 appearances, a fact that shows that students like to comment on assignments of other students and make observations. Furthermore, the great difference in category instructions (I) in threads started by the tutor compared to those started by students (110 versus 42) shows that the basic “channel” in the provision of instructions passes through the tutor, and despite the tutor's encouragement for the exchange of opinions between students, they continue to trust their tutor in the provision of instructions throughout the academic year.

Data analysis

The low appearance of the “functional” categories sending -

receiving assignments (J), sending - receiving grade marks (G)

and notification of advisory meeting (V), appears as expected,

even though here we see the phenomenon of declining

participation, a fact that means that from January and onwards

students turn to more traditional forms for functional

procedures (email, conventional mail, etc.).

Data analysis

It is finally remarkable that the category pointless message (L) mostly

related to messages with wishes for holidays, vacations, etc, is 5th in

threads started by students and 10th in threads started by teachers, a fact

that means that socialization in the student group is a strong parameter

and is (proportionately) high in their ranking during their participation in the

forum.

Data analysis

Data analysisTABLE IV

Number of appearances of message content categories based on modeling in years 2005-8 in INF10 of HOU

Content Category Appearances Number

M 1633

X 1905

P 136

I 152

C 710

D 919

J 809

G 107

V 129

L 194

Total 6694

Data analysis If the above approach is analyzed at the monthly time level, we have

the following results per message content category.TABLE V

M X P I C D J G V L Total

O 381 446 25 37 13 219 184 0 23 43 1371

N 405 479 23 34 129 218 205 19 21 37 1570

D 256 302 18 25 106 139 125 17 18 29 1035

J 71 83 10 6 69 41 35 11 12 10 348

F 92 111 15 8 77 54 46 12 15 12 442

M 86 102 11 9 74 51 41 10 12 11 407

A 75 87 8 7 51 42 35 9 11 9 334

M 71 80 6 6 63 38 33 7 8 8 320

J 111 139 11 11 72 65 57 8 0 13 487

J 28 48 2 2 41 19 18 14 0 3 175

A 21 11 1 2 14 15 15 0 0 2 81

S 36 17 6 5 1 18 15 0 9 17 124

Total 1633 1905 136 152 710 919 809 107 129 194 6694

TABLE VINumber of appearances of message content categories based on modeling in years 2005-8 in INF10 of HOU per month in the threads started by tutors

M X P I C D J G V L Total

O 311 342 25 29 11 167 141 0 21 4 1051

N 328 355 23 25 119 161 153 17 19 5 1205

D 214 258 18 22 98 117 103 15 17 6 868

J 47 55 10 4 61 27 23 8 12 4 251

F 78 97 15 7 72 47 35 11 14 1 377

M 68 74 11 6 67 38 29 9 11 2 315

A 51 58 8 4 49 27 22 6 9 1 235

M 47 49 6 5 55 24 23 5 7 2 223

J 61 75 11 6 62 35 32 7 0 1 290

J 18 31 2 1 33 12 11 11 0 2 121

A 2 3 1 0 7 1 1 0 0 0 15

S 7 9 6 1 1 4 4 0 8 1 41

Total 1232 1406 136 110 635 660 577 89 118 29 4992

Data analysis

There is a similar picture when it comes to threads

started by students related to the subject categories on

which the discussion’s interest focuses (table VII) but

with different intensity.

Data analysis

TABLE VIINumber of appearances of message content categories based on modeling

in years 2005-8 in INF10 of HOU per month in the threads started by students

M X P I C D J G V L Total

O 70 104 0 8 2 52 43 0 2 39 320

N 77 124 0 9 10 57 52 2 2 32 365

D 42 44 0 3 8 22 22 2 1 23 167

J 24 28 0 2 8 14 12 3 0 6 97

F 14 14 0 1 5 7 11 1 1 11 65

M 18 28 0 3 7 13 12 1 1 9 92

A 24 29 0 3 2 15 13 3 2 8 99

M 24 31 0 1 8 14 10 2 1 6 97

J 50 64 0 5 10 30 25 1 0 12 197

J 10 17 0 1 8 7 7 3 0 1 54

A 19 8 0 2 7 14 14 0 0 2 66

S 29 8 0 4 0 14 11 0 1 16 83

Total 401 499 0 42 75 259 232 18 11 165 1702

Data analysis

TABLE VIIIRatios of number of message content categories of threads started by

tutors to the total number and the messages respectively

(B)Appearances Number (inthreads that

beginsfrom the Tutor)

(A)Appearances

Number (total)

B/A in Appearances Number level

(B)Mess. Number (in threads that begins from the

Tutor)

(A)Mess.

Number (total)

B/A in Mess.

Number level

O 1051 1371 0.77 668 882 0.76

N 1205 1570 0.77 659 742 0.89

D 868 1035 0.84 461 597 0.77

J 251 348 0.72 108 164 0.66

F 377 442 0.85 155 218 0.71

M 315 407 0.77 130 202 0.64

A 235 334 0.70 98 172 0.57

M 223 320 0.70 94 159 0.59

J 290 487 0.60 145 274 0.53

J 121 175 0.69 60 94 0.64

A 15 81 0.19 6 10 0.60

S 41 124 0.33 17 28 0.61

In the middle of the academic year a phenomenon is

observed where participation in threads started by the

Tutor declines more than participation in threads started

by students, both in quantity (in number of messages)

and in quality (in appearances of content categories) a

fact that means that fewer students stay in the forum, but

that they are more active.

Data analysis – Conclusion

Thus, the middle of the academic year functions as a

“cross-road” where many students (most of them,

because total participation falls) cease to participate,

while others (fewer ones, because the B/C ratio declines

both in Appearance Number level and in Messages

Number level) participate more actively.

Data analysis – Conclusion

TABLE IXRatios of number of message content categories of threads started by

tutors to threads started by students and messages respectively

(B)Appearances Number (inthreads that

beginsfrom the Tutor)

(C)Appearances Number (in

threads that beginsfrom the Students)

B/C in Appear.Number

level

(B) Mess. Number (in threads that begins from the Tutor)

(C) Mess. Number (inthreads that

beginsfrom the

Students)

B/C in Mess.

Number level

O 1051 320 3.28 668 214 3.12

N 1205 365 3.30 659 83 7.94

D 868 167 5.20 461 136 3.39

J 251 97 2.59 108 56 1.93

F 377 65 5.80 155 63 2.46

M 315 92 3.42 130 72 1.81

A 235 99 2.37 98 74 1.32

M 223 97 2.30 94 65 1.45

J 290 197 1.47 145 129 1.12

J 121 54 2.24 60 34 1.76

A 15 66 0.23 6 4 1.50

S 41 83 0.49 17 11 1.55

Data analysis

The above results which arise from all the data for years 2005-8, are verified at the

annual level, as well as for the current and previous year, meaning that they are

recurrent phenomena.

Data analysis

Questions?

Thank you!

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