Department of Computer and Information Science
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Husain Gholoom - Lecturer
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Home |
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6901 Via Correto
Drive
(512) 712-4407
Statement of Teaching Interests
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Being part of
The Computer Science Department at the
Teaching is a
passion of mine. From my
earliest exposure to the field of computer science, I have respected the
hard work of elucidating and communicating the body of knowledge to
students through rigorously executed instructional material.
As a lecturer, I have translated this respect into action by
designing and producing my own supplemental instructional materials
which have become a valuable asset to my students.
I enjoy challenges and find teaching new students with diverse
educational levels and backgrounds is a great challenge that is
extremely rewarding to
accept, understand and resolve.
Teaching
Experience
My extensive
teaching experience ranges
from teaching introductory level courses such as introduction to
computers science , computer concepts and computer science applications
to more advanced courses such as operating systems and database
management systems. These opportunities allowed me to more and more
exercise and sharpen my
teaching and
programming skills. Although I was a substitute teacher and a
Math teacher during my graduate studies, My real first exposure to
teaching was 12 years a go when I was first hired as a full time
computer science instructor at Public Authority for Applied Education
and Training ( PAAET ) in
Teaching
Philosophy
Simplicity ,
clarity and interaction are my techniques in presenting a course
material. The benefits of
this approach is to attract and retain student’s attention in the class
room . In this context, rather than students spend time writing notes
from the white board, I
prepare handouts and post in
my website for students to print, view and follow during lectures. I
also use slides and extra examples to describe , simplify and clarify
the material. Copies of
slides and extra examples that are used in the class are also posted in
my website for students who miss classes. Homeworks, programming
assignments, projects and grades are also posted in my website for
student to view. I allow students to submit solutions to their
programming assignments and projects
via email.
Interaction
with students in and out of classroom is also one of my teaching
principles. Students
must be active participants in the learning process.
I encourage students to ask questions by asking critical thinking
questions, I pick students randomly in answering questions, I allow
group discussions. Programming assignments are completed individually
while project are sometimes completed in groups. I stimulate students by
asking them to come with answers rather than me providing them with
answers right a way. This process allow students to learn more what they
say rather than learn what they told by others.
I constantly ask students to whether or not the concept is
understood. Sometimes , I have to repeat concepts more than one time and
in various ways in order to convey the subject. In many occasions and as
a way of communicating with students, I gives quizzes in order to
discover whether or not students understood the material .
Computer
science is a problem-solving field and
class material preparation
for this field requires a lot of time.
Although in most cases there are specific text books required for
each class, I sometimes use other
professional resources such as other text books, internet,
journals in order to find extra examples that are essential in passing
the concept. Some examples I
present are drown from other disciplines such as banking, investments,
biology and medicine in order to show the role that the computer plays
in solving those types of problems. I look for feedback from students
and follow professors regarding those extra materials and examples.
Teaching
Interests
With my
computer science experience , I believe that I am qualified to teach
many computer science course clearly. More over, I would be most
qualified courses related to computer programming. For example, several
courses that I am capable of
teaching are computer literacy and the internet,
foundations and fundamentals of computer science, programming
languages such Java / C++, and finally Introduction to Database
Management Systems. List of
all computer science class that I taught are found in
www.husaingholoom.com
. click on the classes link.
I will be keen
on teaching classes in
principles of compiler design and construction. Beside theoretical
compiler concepts, a programming project that involves designing and
implementing a lexical analyzer , parser and intermediate code
generation will introduced. Students will work in teams and
a programming language such as Java or C++
will be used as a tool to implement such project.
Future plans
I am a fond of
teaching by example principle.
My ultimate goal in to start writing a book in Java that
is fully evolves around this principle.
Areas other
than teaching
Beside
teaching, I have a broad array of experiences that I can call into play.
My management background provides a solid foundation for skillful
communication with students and faculty members.
My work experience has taught me to reason analytically and to
use the analytical approach to problem resolution.
As vice chairman of the computer science department, head of the
schedule committee, and head of the computer club, I have developed and
demonstrated strong interpersonal skills.
I have worked closely with other university departments and
students on technical projects, delivered numerous presentations and
have been involved in many extracurricular activities.
My technical
skills have been applied to the design and implementation of a faculty
member database system for our entire organization, a web page framework
for the College of Business Studies, and a last class timetabling for
the Department of Computer Science.
I am confident
that this extensive technical and teaching skills will allow me to
contribute immediately to the strong reputation of the university and to
the computer science department. Sincerely, Husain Gholoom |
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College Timetabling
Information System
Husain Gholoom (
Instructor )
Department of Computer
and Information Science
College of Business
Studies
Public Authority for
Applied Education and Training
huaingholoom@yahoo.com
Abstract
Course
timetabling is an important operational
and time consuming task in
many colleges and universities. Every lecture room must be assigned to
instructors and their classes , day and
time for each lecture without violating any constraints [ 1 ].
The purpose of this paper is to examine
timetable generation problem with regards to college classrooms , course
subjects, start and end time for each subject.
Several popular
techniques used on
course timetabling problem are discussed.
The paper will introduce a brief description of the timetabling problem
at college of business
studies at Public Authority for Applied Education and Training
in
1.
Introduction
Timetabling is usual problem of each academic community and it was very often
tried to be solve by researchers. Although a lot of scientific and commercial
work was made in this area, however , in many educational institutions,
timetable is still scheduled manually and often if a computer is used, it
is needed only for data presentation or checking constraint validation [ 2 ] .
The term scheduling is generally described as
constrained allocation of
resources to objects being placed in space-time with minimal cost of
set of resources used, however , timetabling is the allocation subject to
constraints of giving resources to objects being placed in space-time which
satisfies or nearly satisfies set of possible objectives [ 3] .
Example of scheduling is the airplane or a train
route traveling from point a
to point
b
with
objective of minimizing the
total cost function, where class timetabling and exam timetabling is examples of
timetabling which all hard constraints and few soft constraints must be
satisfied. Constraints are
restrictions of some type such as number of
lecture rooms, number of
computer labs. Constraints are
either hard or soft. Hard
constraints are conditions that can not be violated under any circumstances and
soft constraints, on the other hand
are constraints that are
desirable, may be violated, but
should be satisfied as much as
possible [4] .
Many techniques has been used in order to automated
timetabling problem . In the early days and still some institutions carry
out the construction of time tabling manually every semester .
The manual task
is complicated and time consuming since the educational system became
more dynamic and complicated.
The need for automated timetabling system became a must. New techniques
were presented such as integer and linear programming ,
graph coloring , genetic algorithms , memetic algorithms ,
local search, and constraint logic programming . It should be noticed
that once the timetable is generated , the timetabling team members may
interactively change the timetable that is produced by the automatic tool
without violating any of the hard or soft constrains . In this paper, we present
college timetabling problem
and overview of automatic and
interactive techniques and tools applied in solving timetabling . The paper will
also presents a brief description of timetabling problem at college of business studies at
public authority for applied education and training. Finally,
a broad information system architecture for automated timetabling problem
will also be presented.
2.
Course Timetabling Problem Description
Course timetabling
problem , which is one classification of university timetabling , is well known
problem for researchers for some time. The timetabling problem is dealing with
generally series of
meetings between students,
instructors in classrooms or labs (
space ) for a specific period of time
per week ( timeslots ) during a semester
according to satisfied
constraints [ 5 ]. It is mapping
between instructors meeting students in a specific location for a period of time
in specific days of the week during a semester.
College of business
studies at public authority for applied education and training
consist of two separates main campuses, all male and all female campuses. Course
timetabling is done manually each semester
and each department
traditionally leads it by select few faculty members each academic year to
perform the task. Since teams
changes every academic year, each team may have their own different
methods of timetabling depending on
their professional background. Each department is assigned its own classrooms
and labs by the college administration. Each department uses their own policies
when constructing timetable. There are ten different department covering four
primary disciplines . Once the timetable is completed by each department, it is
handed to the regenerator’s office where it is entered into a centralized
computer for students to view and register. This task is very complex , time
consuming , and it carries high
potential for errors and
time conflicts . It usually takes anywhere between 2 – 4 days for each
department to complete the course
timetabling for their faculty members. Students at
Courses vary when it
comes to number of hours for each course. Courses carry anywhere between 1 hour
to 7 hours depending on the department. For example computer science department
has classes that hod anywhere between 3 to 7 hours per week. While English
department have classes that carry anywhere between 1 to 4 hours per week. Each
course has a department id, course id, course
name, section number, number
of hours. Date , time, room number, location ( Male / Female college ) , and a
professor’s name will be added during timetable construction .
The following table shows
some of the allowed times for scheduling classes at the computer science
department.
|
0 |
S T Th
8:00 -
9:00 |
9 |
S T Th
8:00 -
10:00 |
|
1 |
S T Th
9:00 -
10:00 |
10 |
S T Th
10:00 -
12:00 |
|
2 |
S T Th
10:00 -
11:00 |
11 |
S T Th
12:00 -
14:00 |
|
3 |
S T Th
11:00 -
1200 |
12 |
S T Th
14:00 -
16:00 |
|
4 |
S T Th
12:00 -
13:00 |
13 |
M W 8:00
- 9:30 |
|
5 |
S T Th
13:00 -
14:00 |
14 |
M W 9:30
- 11:00 |
|
6 |
S T Th
14:00 -
15:00 |
15 |
M W 12:30
- 14:00 |
|
7 |
S T Th
15:00 -
16:00 |
16 |
M W 14:00
- 15:30 |
|
8 |
S T Th
16:00 -
17:00 |
17 |
M W 15:30
- 17:00 |
Observing the foregoing
table, it is safe to assume that, a slot can be divided into half-hour.
Therefore, a class scheduled on M W 8:00 - 9:30, will reserve 6 slots. Only five
classrooms and nine computer labs are
assigned to the computer science department.
3.
Courses and Constraints
The timetabling problem
is dealing with generally series
of meetings
between students, instructors in classrooms or labs (
space ) for a specific period of time
per week ( timeslots ) during a semester
according to satisfied
constraints . constraints can be classified as hard
or soft [ 5 ] . the following
are examples of hard and soft
constraints that appears at
a.
Female instructors can
only teach at the female college . They have high priority in timetabling for
all female college [hard] .
b.
Practical
courses that require hands on and practice must be taught in labs [hard].
c.
Instructor
must teach one class at any
given time and a class is taught by one instructor only[hard].
d.
Conflicts :
Lectures of courses in the same curriculum or taught by the same teacher
must be all scheduled at different times [hard].
e.
Limited number of classrooms and labs
of particular type [hard].
f.
Classrooms
Occupancy : classrooms can
not be shared in a specific time by more than one class or instructor [ hard ].
g.
Length of classes can
be scheduled for 60 , 90
, 120 minutes
only [ hard ] .
h.
Male
instructors are allowed to teach in all female college
[ soft ].
i.
Instructor’s teaching
load varies. Department head , Phd holders,
non PHD holders , and Lab
Instructors all have different
teaching loads [ Soft ]
j.
Number of hours for each class : class could carry 1
. . 7 hours per week [soft]
k.
Room Capacity : The
number of students that attend a course must be less or equal than the number of
seats of all the rooms that host its lectures [ soft ].
l.
Classroom
/ Lab utilization : The daily
schedule of a classroom or a lab should be as much compact as possible, avoiding
unused timeslot [ soft ].
4.
Timetabling
Automation Techniques
There has been a large amount of research carried out on university and
college timetabling problem. Since the size and the complexity of current
universities has increased, basic domain-specific
heuristic methods no longer can construct good timetables. The trend
shifted toward constructing more general and automated algorithms solving
timetabling problems, such as genetic algorithms, memtic algorithms ,
evolutionary or meta-heuristics algorithms represented in simulating annealing
and tabu algorithms. Constraints Logic are also popular approaches in the area
of artificial intelligence, operational research and logic programming. It seems
that from the following brief description of automated mythologies, the most
popular technique is usaing constraints approaches followed by the
Evolutionary and Genetic algorithms. One can first use heuristics in
order to constraint the search for an initial timetable solution. Then , using
Genetic Algorithm as an enhanced technique on the initial solution in order to
develop the solution and arrive to a
final and optimal solution. Bare in mind that there is not an exact technique
for an optimal timetable that is generated by an automated general
timetabling system.
4.1
Genetic Algorithms
Genetic algorithms
(GAs) are based on finding greatest numbers of feasible
solutions then selecting the best one with the least number of
constraints violations. Long bit string encoding regarding when and where each
class is to take place is the most common genetic representation for a
timetable. When selecting pairs of
timetables they might be crossed over and as a result ,
the bit string is sliced
and new timetables are created.
Researchers have implemented intelligent systems that
uses a smarter mutation operator than the cross over mythology.
Other GA timetabling systems
represent timetabling as an order of events and
the order of events are inputted into special program which uses the
orders in order to produce the timetable.
GA has been implemented that deals with infeasible solutions as well.
Population of individual solution was selected and uniform cross over and
transformation operators was applied.
Detailed description of this method found in [ 1 , 6 , 10
]
4.2
Memetic Algorithms
An one extension of
GA’s is Memetic algorithms. Memetic algorithms
based on a representation of how ideas progress. The basic units of ideas
are memes, which can be improved in order to reach a local optima during their
lifetime unlike gene which can not improved during their life time.
Thus to ensure that all population members of
the timetable are at local
optima, a local search method in the form of
hill climbing or repairing strategy function is used at set
intervals [1]. One draw back of this particular search technique is the
time that the search takes in order to reach a solution.
Paechter and Cumming implemented
Memetic lecture scheduling system that has been used at
4.3
Simulated Annealing
Simulated Annealing
(SA) is arbitrary search technique
for finding solution to optimization problems. It is based on correlation with
the physical development of annealing, which involves the aggregation of many
elements in a physical system as it is cooled. Initially ,It was first used as a
technique to solve hard non linear optimization problems .
In this method , optimization is performed without former knowledge of
problem structure or of any particular solution approach. Most of the time, this
method is easy to apply because changes or moves in the solution space of the
problem that is being solved are
easy derived. Changes results to
either uphill or downhill arrangements in the solution space. The idea is
representing college timetabling by
placing teachings into periods of days of the week so that number of conflicts
for resources such as classes, teachers , classrooms are minimal. The algorithm
is identified as geometric ,
adaptive , and adaptive with reheating cooling scheduling systems are
applied to college timetabling problem.
4.4
Tabu Search
Similar to Simulated
annealing, tabu search starts in the same way as common local search advancing
iteratively from one point (solution) to another until a chosen termination
criterion is satisfied. Local search are based on the notion of neighborhoods.
There is a tabu list that is maintained by tabu search which represent
timetables that have been visited recently and are forbidden.
The purpose of this list is prevent cycling moves and prevent the search
from staying in the same area and thus escape from the best possible solution.
Usually tabu list is a fixed size
list, When a new move is added to the list
the oldest one is removed from that list so that the moves will not be
revisited periodically. In some cases, tabu moves may prevent the search to
proceed in order to fine new improved solution. thus,
If tabu timetable best solution is reached
“ aspiration level “ , the solution might me removed from the tabu list.
More detail are found in [ 6 , 15 , 16 , 17 ]
4.5
Constraint Logic Programming
One way to solve
timetabling problem is by using constraint logic programming ( CLP ) over finite
domains. Constraints in timetabling problem
are formulated as set of distinct variables in a declarative manner
within the domain. The general idea
is to assign values to variables satisfying all the constraints. Constraint
Logic Programming can also be viewed as
logic programming in which unification if being replaced by constraints
handler in a constraint system. The power of this approach clearly demonstrated
in languages such as Prolog , CLP , CHIP , CHR ( Constraint
Handling Rules ) , and DOMLOG . For example, clauses in Prolog
are equivalent to
set of constraints in CLP ,
satisfying these constraints are verified
during execution of the program. In constraint Logic Programming CHIP for
example, the starting time for each course is represented by a domain variable
and can represented by numbers. Attributes such as classroom location
of a course may also be a domain variable and can be represented as
numbers. All constraints of the timetabling problem can be symbolized by
built-in constraints . Search is
conducted in finding solution and backtracking is performed when conflict is
detected. Many software tools have
been developed for automated timetabling.
More details regarding these methods are in
[ 1 , 6 , 8 , 9 , 10 ] .
5.
An Automated Timetable
Information System Framework.
Timetabling system
production is very complex task and consist of many steps such as data
gathering, data entry, data verification.
One of the steps in timetable production system is timetable creation.
Since timetable creation is a very complex activity and time consuming, the use
of software helps carrying it out and reduces time . The automated ideal
timetabling production system should consist of databases to store data, web
site for data entry , timetable view and maintenance, timetable algorithms for
data entry, data verification and timetable construction. Files are used in
order to store input data and output results. When dealing with files as input,
problems dealing with data entry are encountered.
Once data are entered
incorrectly, unexpected behavior of the timetable creation algorithm software
might be encountered or the software might terminated abnormally. Incorrect data
are very hard to detect and sometime it requires to be checked manually. One way
to solve this problem is automation checking and data validation upon data
entry. Bad data should be rejected by the automation data verification software.
Another problem is system modifications to constructed system software
due to change in user requirements.
The problem exist when the system is gigantic and closed. In order to
avoid this problem, The system should be adaptable and easy to expand
[18].
In the following
sections, an open and easy to expand system workflow will be presented. The
system performs data verification in the early stages, databases are used for
data storage , website is used for
data entry , data validation according to the specified business rules before it
is stored in the data base. Website can be used as well for information
retrieval.
5.1 The timetabling Production
System
This system might
contain two main components. The first component is the system for data handling
such as data entry , verification, saving .. etc. This subsystem might contain
more than one module. Each module responsible for specific task and deal with
specific file or database. For
example, instructors data instructors are saved separately from classes data.
The second component of the system is the timetable construction algorithm. This
also might contain several modules and each module is responsible for specific
task. For example, printing hard copy of timetable schedule module is different
from constructing the actual timetable schedule module which is also different
from posting the schedule on the web module.
( See figure 1 shows each
department module )
5.2 Data Gathering and
preparation
the process of
timetable production start when at the beginning of each semester, the
instructor is handed the class request form to indicate the classes that he /
she would like to teach next
semester. The process ends when the instructor received his / her class schedule
for the next semester. The entire process takes about 2.5 to 3
weeks to complete. various data are entered into the system , for example
data about the course that is offered such as time, location, number of hours ,
days of the week , maximum number of students allowed to enroll
… etc. this information is
entered into the system each semester. data about instructors are also entered
into the system such as teaching status ,
class preference, days and time, class to be taught ..
etc. external instructors are not fed into the system. Classes without
assigned instructors are posted as staff and will be entered into the manually
at later stage. Most of this
information remains unchanged during each semester .
This data must be verified and stored in data files ( databases ). The
choice of databases are implementation decisions. Exams timetables are not part
of this system.
During timetable
construction, hard and soft constraints will be taken into considerations. The
algorithm might run more than one time in an iterative nature until an couple of
satisfying solutions are found. Once a satisfied timetable is constructed,
instructors will be able to view
them on paper or on the web . Modifications are done via the timetable
responsible team only members. Data entry and database modifications are done
via administrative staff with the timetable team authorization.
6.0
The Software Design
software design will
describe the elements of the system and how these elements interacts with each
other. Since the system is very complex, We are going the break down the
elements of the system into small
parts. This allows the system to be easier to be solved. Once each part is
implemented, the combination of these parts will represent the solution to our
complex system. the system will consist of
three major layers, interface , logic, and data persistence layers. More
example of actual implementation of such systems found in [ 3 , 19 , 20 , 21 ] .
6.1 The Interface Layer
First layer is the
interface layer which presents data to the user. This layer allow the user to
enter data and the data is exchanged with other layers. One typical
type of data in this layer is basic data that belongs to the institution.
This data must be secured and the users are not allowed to modify any part of it
such name of departments, department codes, class codes, lecture room numbers ..
etc. Another type of data is direct input data which is performed by
administration staff. This data includes instructors preference classes to
teach, preference time .. etc. The data is validated as it is entered into the
system. Any error must be corrected before it is stored. Last type of data
involves the timetable responsible team which monitor the system in order to
produce a solution without violating any constraints. This layer only interacts
with the logic layer. for example, If database change is required , it must go
thru the logical layer first then the data resolution layer.
6.2 Logical Layer
The second layer is the
logic layer. This layer is for data validation and allowing the data to be
exchanged with other layers. The layer also responsible for enforcing the
business rules of the system.
business rules do vary and are defined by each institution. These rules are
coded in this layer. Examples of
business rules are female instructors are not allowed to teach at boys college ,
starting time of first class in the morning, the starting time of the
last class in the afternoon . etc.
6.3 Data Resolution
This is the physical
storage of data in files and databases
in a specific format after it has been generated such as text files,
databases format, html format .. etc.
7.0 Technology Resources
In order to implement
such a system the following technology needs are required. First , A language to
implement the data entry forms such
as Microsoft access , oracle forms ,visual basic , html, php, and perl. Most of
these languages are widely used in creating web pages and web applications.
Second, the form in which data are
interchanged between layers such as
text files , sql , and xml. Third,
language to implement the algorithm such as java . bear in mind that the
language must be platform independent. Java is widely used in many different
platforms with various operating system. Finally,
a tool to manage the databases such as mysql and sql database. These
tools should be used by any programming languages as long as the programming
language has a capability of connecting to the database via ODBC ( Open DataBase
Connectivity ). Java has such capability by using JDBC ( Java DataBase
Connectivity )
When it comes to the
interface layer, One must a browser that is available in every platform, a web
browser such internet explorer , safari or fire Fox are found in most platforms
and could be used in order to input or modify
data.
In the logic layer ,
since web is going to be used a server is required. Apache HTTP as web server
and Jakarta Tomcat as server to provide the business logic and are widely used
in systems such as UNIX , Windows and MAC. Since this is a web application,
security is very important.
In the data resolution
layer, JDBC ( Java DataBase Connectivity ) could be used in order to store and
retrieve data from and to database via SQL language. Sql is standard database
language that is used by most database programming languages such DB2 by IBM and
Oracle.
8.0
Conclusion.
The long and demanding
task of creating a timetable for a college can be considerably eased by the use
of computers and the automation process . However, the substantial complexity
and variety of the problem means that there is much room for improvement on
current systems. In this paper , we presented timetable generation problem with
regards to college classrooms , course subjects, start and end time for each
subject. We started by showing
several popular
techniques used on
course timetabling problem. Such techniques were genetic algorithms,
memtic algorithms , evolutionary or meta-heuristics algorithms represented in
simulating annealing and tabu algorithms. Constraints Logic which is also
popular approach in the area of artificial intelligence, operational research
and logic programming was explored. The paper introduced briefly the timetabling
problem at college of
business studies at Public
Authority for Applied Education and Training in
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