3rd International Conferences and Workshops on Basic and Applied Sciences 2011
ISBN: 978-979-19096-1-7
A Model Development of Tutorial Scheduling System Through Decision Support System in Universitas Terbuka: A Case Study in Surabaya Regional Technical Unit Pismia Sylvi Department of Statistics Universitas Terbuka, Tangerang, Indonesia e-mail:
[email protected] in learning, students can request for information or face-to face tutorial assistance to the UT's regional technical unit in their area. UT has 37 regional technical units to provide day-to-day distance learning services and one of them is Surabaya Regional Technical Unit.
Abstract The large number of basic education program students, tutorial locations that are spread in many districts/cities, and the limited amount of tutors who have appropriate competency, makes the face to face tutorial scheduling in Univcrsitas Terbuka requires much time and involves many officers. Fixing schedule often have to be done repeatedly when (1) there are one or more tutors who are giving tutorial in different classrooms or different subjects al the same time and (2) there are tutors who clashed time teaching (different tutors provide tutorial in one class at the same time). By using Decision Support System, scheduling is more efficient because it is done by developing the logical relationship underlying the decision making process. The method used is Constraint Programming, which sought solutions through a mathematical calculation based on prerequisites and limitations (constraints) that have been assigned to the tutorial scheduling. However, the lack of tutors who have according competence remains a major obstacle in this new system, as was the case in the old system.
Specifically for basic education program students Elementary School Teacher Education (PGSD) and Education Teacher Children Years Early (PGPAUD), they are provided face-to-face tutorial as learning support service for difficult subjects and subjects with practice/practicum. To conduct this face-to-face tutorial, UT's regional technical unit has to recruits tutor and makes tutorial schedule. Tutorial scheduling is to arrange tutors and tasks in order to make them placed appropriately. Surabaya Regional Technical Unit has the large number of basic education program students which spread in 18 districts/cities. In each district/city, every students from the same program and the same semester will be grouped (about 30 students in a group). Tutorial is provided for two, three, or four subjects depends on the program and semester. Since there is a limited amount of tutors for lots of tasks, tutorial scheduling as a decision problem becomes harder to solve, takes much time and involves many officers.
Keywords: Tutorial scheduling, Decision Support System, Constraint Programming
1
Some papers can be found related this scheduling system are only for one location (campus) of a conventional university. The objective of this paper is to develop a decision support system for tutorial scheduling system in UT's regional technical unit, especially in Surabaya, that could cover the deficiencies in the old system.
Introduction
Universitas Tcrbuka (UT) is a higher education institution in Indonesia which applies a distance and open learning system. In UT, students arc expected to learn on their own initiative and UT provides them with learning materials specifically designed for independent learning. Aside from using materials provided by UT, students can also take the initiative to make use of the library, take tutorials (whether face-to-face or through the internet, use radio or television broadcasts), or use and materials learning computer-assisted audio/video programs. When faced with difficulty
2 Methodology 2.1
Decision Support System (DSS)
Decision Support Systems are interactive computerbased systems, which help decision makers utilize data and models to solve unstructured problems
M009
Pismia Sylvi, A Model Development of Tutorial Scheduling System through Decision Support System in Universitas Terbuka: A Case Study in Surabaya Regional Technical Unit
[Scott Morton, 1971]. Keen and Scott Morton (1978) mentioned that DSS couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semi-structured problems. Scientific approach to DSS is: (1) Define the problem, (2) Classify the problem into a standard category, (3) Construct a mathematical model, ( 4) Find and evaluate potential solutions to model, and (5) Choose and recommend a solution to problem (Turban, 2005). 2.2 Constraint Programming According to Bartak [2003], the difficulty of academic scheduling as a decision-making activity is that neither the structure of the resources nor the structure of the tasks is homogenous and many set constraints must be assumed to model the problem. Constraint Programming (CP) provides technology to model and solve such real-life problems by a calculation approach or a mathematic computation of problems related constraints of variables [Muhyi, 2008]. The basic idea is to model the problem as a set of variables with domains (a finite set of values for the variables, typically denote alternative decisions to be taken) and a set of contraints restricting the possible combinations of the variables' values. This model is called a Constraint Satisfaction Problem (CSP) and a solution to that CSP is an assignment of variables to values which satisfies all constraints [Bartak, 1999].
In Bartak (1999], a CSP is defined as: •
a set of variables X={ X1, •.. ,Xn},
•
for each variable x;, a finite set D; of possible values (its domain), and
•
a set of constraints restricting the values that the variables can simultaneously take.
Note that values need not be a set of consecutive integers (although often they are), they need not even be numeric. A solution to a CSP is an assignment of a value from its domain to every variable, in such a way that all constraints are satisfied at once. We may want to find: •
just one solution, with no preference as to which one,
•
all solutions,
•
an optimal, or at least a good solution, given some objective function defined in terms of some or all of the variables.
Solution to a CSP can be found by searching (systematically) through the possible assignments
2
of values to variable. Search methods divide into two broad classes, those that traverse the space of partial solutions (or partial value assignments), and those that explore the space of complete value assignments (to all variables) stochastically.
2.3 System Development According to Turban [2005], a system development project encompasses all the activities undertaken from the time at which a potential requirement is identified until the resulting system is fully implemented and accepted by the end user. The process can involve many stages over a long period. The System Development Life Cycle (SDLC) framework provides a sequence of activities for system designers and developers to follow. It consists of a set of steps or phases in which each phase of the SDLC uses the results of the previous one; those phases are, (1) Project planning: initiate, ensure feasibility, plan schedule, obtain approval for project, (2) Analysis: understand business needs and processing requirements, (3) Design: define solution system based on requirements and analysis decisions, (4) Implementation: construction, testing, user training, and installation of new system, and (5) Support: keep system running and improve. The SDLC framework is similar to problem-solving approach, as applied in this research: Cl) Organization recognizes problem (Project Planning), team investigates, (2) Project understands problem and solution requirements (Analysis), (3) Solution is specified in detail (Design), (4) System that solves problem built and installed (Implementation), (5) System used, maintained, and enhanced to continue to provide intended benefits (Support) - isn't discussed in this paper.
3
Result
3.1 Current Tutorial Scheduling System Surabaya Regional Technical Unit has to conduct a face-to-face tutorial in two stage for every exam period, because of large amount of basic education program students. First stage is for advanced semesters' students and second stage is for new students. Before making schedule, the team should identify how many groups in every semester per program in each district/city. Base on this data, the team can set tasks and predict how many tutors are needed. The tutorial scheduling team consists of eight officers as members, and one of them as the supervisor. To arrange the tutorial scheduling, 18 districts/cities in Surabaya Regional Technical Unit are devided into 4 zones; Zone I is Surabaya, Sidoarjo, Mojokerto, and Jombang; Zone Il is Madiun, Ponorogo, Magetan, and Ngawi; Zone III
3rd International Conferences and Workshops on Basic and Applied Sciences 20 I I
is Bojonegoro, Tuban, Lamongan, and Gresik; Zone IV is Bangkalan, Sampang, Pamekasan, and Sumenep. Every two member will make a tutorial schedule draft for one zone, and then give it to the supervisor. The supervisor will join these four schedule drafts and check it wheter (1) there are one or more tutors who are giving tutorial in different classrooms or different subjects at the same time and (2) there arc tutors who clashed time teaching (different tutors provide tutorial in one class at the same time). If the supervisor still find these problems, he has to fix it by replace those tutors or reschedule them, usually it has be done repeatedly before getting the final tutorial scheduling. The complete procedure to conduct this face-to-face tutorial until (from planning monitoring - evaluation) is shown in Figure 1.
(2)
(3) (4)
(5) (6)
(7)
ISBN: 978-979-19096-1- 7
there are 10 semesters in PGSD program and 9 semesters in PGPAUD program; each semester in every program has specific tutorial subjects; groups are named with capital letter (A, B, C, and so on) depend on the number of groups in every semester per program in each district/city; each group gets at most 3 subjects in one day; each tutor gives at most 3 subjects in one day; tutor can't be assigned for more than one district/city.
3.2.1 Dimension In mathematical calculation, dimension use integer index to present its elements. If dimension D is a set of all letters, then D = { a, b, c, ... , z}. In CP calculation, D becomes an array then:
•
•
D[OJ = a
•
D(l) = b Df2l = c
•
D[23]
•
=z
To define a CSP, dimension in tutorial scheduling is presented in variable and its domain as below. (1)
City/District variable (C), its domains i.e. (1.1) Surabaya (1.2) Sidoarjo (1.3) Mojokerto (City) (1.4) Mojokerto (District) (1.5) Jombang (1.6) Madiun (City) (1.7) Madiun (District) (1.8) Ponorogo (1.9) Magetan (1.10) Ngawi (1.11) Bojonegoro (1.12) Tuban (1.13) Lamongan (1.14) Gresik (1.15) Bangkalan (1.16) Sampang (1.17) Pamekasan (l.18) Sumenep.
(2)
Program variable (P), its domains i.e. (2.1) PGSD (2.2) PGPAUD.
(3)
Semester variable (S), its domains i.e. (3.1) 1s• semester (3.2) 2"d semester (3.3) 3rd semester (3.4) 4th semester (3.5) 5th semester (3.6) 6111 semester (3.7) ih semester
Figure 1: Current Tutorial Scheduling System Flowchart 3.2
Problem and Solution Requirements
Problems that arise in the current tutorial scheduling system are much time to fix the tutorial schedule (about 5 weeks) and many officers (in team) involved to make this schedule. That team has to arrange tutors allocation, i.e. assigning tutors to subjects, and tutor scheduling, i.e. ordering of tutorial time at each tutor. To fix the schedule draft and the final schedule, they have to do repeatedly. A new tutorial scheduling system that can solve these problems becomes a necessity. Limitations (constraints) in this tutorial scheduling J.C.
(1)
tutor can only be assigned with subject/subjects in accordance with his/her competence;
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Pismia Sylvi, A Model Development of Tutorial Scheduling System through Decision Support System in Universitas Terbuka: A Case Study in Surabaya Regional Technical Unit
(3.8) 8th semester (3.9) 9th semester 1 (3.10) l0 h semester. (4)
Group variable (G), its domains i.e. (4.1) A (4.2) B (4.3) C and so on depend on the number of groups in every semester per program in each district/city.
(5)
Subject variable (M), its domains i.e. (5. l) Keterampilan Berbahasa Indonesia SD (code: PDGK4101) (5.2) Metode Pengembangan Kognitif (code: PAUD4101), and so on.
(6)
Tutor variable (T), its domains i.e. Frida Dorintan (6.1) Nurmi (IDN: 71000001) (6.2) Dyah Argarini (IDN: 71000003), and so on.
(7)
Tutorial Time variable (H), its domains i.e. (7.1) I (7.2) II (7.3) llf.
3.2.2 Relationship between Dimensions These relations are: Tutor-Subject (TM) is assigning tutor to (1) subject, e.g. • Nurmi Frida Dorintan (71000001) has subjects in accordance with her competence i.e. Metode Penelitian (IDIK4007), Tugas Akhir Program (PDGK4500), Pendidikan Lingkungan Hidup (PEBI4223) • Dyah Argarini (71000003) has subjects in accordance with her competence i.e. Metode Penelitian (IDIK4007), Bahasa Inggris untuk Guru SD (PDGK4304), Penulisan Karya Ilmiah (PDGK4402). (2) Program-Semester (PS) is semesters in a program, i.e. • PGSD I" semester • PGSD 211d semester • PGSD 3'd semester • PGSD 41h semester • PGSD 5111 semester • PGSD 61h semester • PGSD 71h semester • PGSD 8th semester • PGSD 9th semester • PGSD 10th semester • PGPAUD 151 semester • PGPAUD 2"d semester • PGPAUD 3rd semester • PGPAUD 4th semester 4
(3)
(4)
• PGPAUD 5th semester • PGPAUD 61h semester • PGPAUD ?1h semester • PGPAUD gth semester • PGPAUD 9th semester. Program-Semester-Subject (PSM) is tutorial subjects for a semesters in a program, e.g. • Tutorial subjects for PGSD I" semester are Keterampilan Berbahasa Indonesia SD (PDGK4101), Konsep Dasar IPS (PDGK4102), Konsep Dasar IPA (PDGK4103) • Tutorial subjects for PGSD 2nd semester are Strategi Pembelajaran di SD (PDGK4J 05), Praktikum IPA di SD Matematika (PDGK4107), (PDGK4108). City/District-Semester-Program-Group (CSPG), is groups in every semester per program in each district/city, e.g. • PGSD 2nd semester in Surabaya has 2 groups, i.e. A and B • PGSD 2nd semester in Sidoarjo has 3 groups, i.e. A, B, dan C.
Value for element of array is 1 (one) if there is a relation between dimensions' elements, and O (zero) if there is no relation between them. E.g. M[O] = PDGK4101 M(l] = PDGK4102 M(2] = PDG1Al03, and so on. P(O] = PGSD P[l] = PGPAUD, Then relation between Subject (M) and Program (P) can be written in array form as: MP[O][O] = MJ[PDGK4101][PGSD] = 1 MP(l](OJ = MJ[PDGK4102JlPGSD) = 1 MP[O][l] = MJ[PDGK4101][PAUD) = 0, and so on.
3.3
CSP Solution
To get a solution for a CSP, a solution variable X has to define i.e. XfCity l [Program] [Semester l[ Group] [Subj eel] [Tutor][Time] or X[C][P][S]fG][M][Tl[Hl Same as before, X is equal 1 (one) if there is relation between dimensions' elements, and O (zero) if there is no relation between them. When X is equal 1, it becomes a solution for tutorial scheduling problem. The variable X should meet all constraints on the relationship between variables before, which means that the variable X is worth less than or equal tothe corresponding relationship between variables. Suppose a data on Subject-Tutor (SM), then the variable X must satisfy
3rd International Conferences and Workshops on Basic and Applied Sciences 2011
X[C]f P][S][G][M][TJ[H]
s SM[s][m]
In this way, it can be guaranteed that the variable X will always be consistent with the limitations specified. The next step is to set limits or conditions (constraints) as follows. a. X is an integer value, whose (zero) or 1 (one), formulated as
value is O
0:;; X[C]fP][S][G][M][T][H] s 1, XE int
(1)
b. For each value of X should be less than or equal to the data corresponding on the relationship Subject-Tutor, defined as XlCJ[PlfS][G][Mm)[Ti]lH]:;; MTf m][t]
(2)
3.4 Implementation There are five databases used in this application, i.e. the Tutor database, the Tutor-Subject database, the Group database, the Subject database, and the other database. These five databases arc to be input in the process of scheduling a tutorial, as well as storage if there arc any changes to existing data. An databases' outline of tutorial scheduling can be seen in Figure 2. /n..,,0a1at>aoe
f
(
\
lncludo
,N,m"•"""'"r.o·.it.!11t(• Mllm!IC>'otOOno:�POl'/IO:"I""'
\
l
{Tuk>rial Schedule): -City/Oi$trlCI OulpUt - Tutorial Locabon
Tuwnsl Time
I
1
.::.,I
.r\
j
\
, �m�"!!;'!;:,,,ldoD'o \ J \ �:;7,orur Sia" n ,,,.,..c,ty.> \j
Figure 2: Database
XfCc][Ppj[Ss][G8)fM][T][H]:;; CPSG[c](p]fsl[g](S)
f. Each group gets at most 3 different subjects in one day. In other words, the value of X for certain program, certain semester, certain group, certain subject, and certain tutorial time should be less than or equal to 1 (one), formulated as
Ipsgmh X[C][Pp][Ss][Gg][Mml[TJ[Hh]:;; 1
Then the Supervisor can apply the new system to make a tutorial schedule whithin few minutes. And the example of output can be seen in Figure 3 and Figure 4.
(6)
g. Each tutor gives at most 3 subjects in one day. In other word, the value of X for certain tutor, certain subject and certain tutorial time should he less than or equal to 1 (one), formulated as
h. Tutor can't be assigned for more than one district/city. In other word, the value of X for certain tutor and certain city/district should be less than or equal to 1 (one), formulated as X[Ccl[P][S][GJlM][Tt][H]:;; 1
(8)
From this CSP, solution can be obtained by developing application using C# language.
Figure 3: Tutorial Schedule Recapitulation One problem still arises in this new system is the lack of of tutors who have according competence, so there are some tasks in this tutorial schedule have no tutor.
5
Pismia Sylvi, A Model Development of Tutorial Scheduling System through Decision Support System in Universitas Terbuka: A Case Study in Surabaya Regional Technical Unit
_
[6]
.
:r.-t-�;.fOllll,!t'..u.-....:....:..�".w:���·I ... ....
......�-_
�,:'l(�l(;i;'IIT.r'�-'fr"�·1
_ _ _
,_ ...---..._.._, ....-·,.�:..,·......._,.-· ,, ...... _.., ..--·------··
Figure 4: Tutorial Schedule For PGPAUD 3ro Semester in Bangkalan 4
Conclusions
To get a final tutorial scheduling in the old system needs much time to fix it and many officers (in team) involved. By using Decision Support System in the new system, scheduling can be done only in few minutes by one person. However, the lack of tutors who have according competence remains a major obstacle in this new system, as was the case in the old system.
References [1 J
Bartak, R., Constraint-based Scheduling: An Introduction for Newcomers, In Kadar, Monostori, and Morel (eds.) Intelligent IFAC 2003, Systems Manufacturing Publications, Elsevier Science, pp. 69--74, 2003.
l2J
Bartak, R., Constraint Programming: In Pursuit of the Holly Grail. Proc. of the Week of Doctoral Students (WDS99), Part IV 1999, pp.5?5--564, 1999.
[3]
Keen, P.G.W., and M.S. Scott Morton, Decision An Support Systems: Organizational Perspective, Reading, MA: Addison-Wesley, 1978.
[4]
Muhyi, Y., Penjadwalan Kuliah Otomatis dengan Constraint Programming. Seminar Nasional Teknologi Informasi (SNASTI) 2008, STIKOM Surabaya, 2008.
[5]
Scott Morton, M.S., Management Decision Systems: Computer-Based Support for Decision Making, Cambridge, MA: Harvard University, Devision of Research, 1971.
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Turban, E., Aronson, J.E., and Liang, T.P., Decision Support Systems and Intelligent Systems-r" Ed, Pearson Education, New Jersey, 2005 .
