An Adaptive Advisement System for Information Technology Students

by | Apr 18, 2014 | Broadening Participation, Cybersecurity, Educational Technology, HCI

A Conceptual Design

This article was written for an assignment in EDG6931 Human-Computer Interaction in Spring 2014 at the University of Florida.

Purpose

The purpose of this project is to design an interface for an adaptive, intelligent, advising system for Information Technologies (IT) students.  There are many career paths and options for IT majors.  In addition, IT degree track programs overlap in many areas.  Students who come in to the program believing they want to become computer programmers may later determine they are more interested in database design or cybersecurity.  Often students find themselves at the end of a degree path only to discover they are more adept or interested in another area of IT.  

Traditionally we have advised students by asking them what degree they would like to complete, and then setting them on a path to complete that degree.  The concept behind this project is that students may be better advised by looking at their interests, skills and course experiences and then selecting a learning path that aligns with those elements.  The learning path can then be adjusted as the student completes curriculum and further refines their interests and skill strengths. The final outcome will be more customized to the student, and as such, will hopefully allow them to be more successful upon graduation.  

Much of the motivation for this conceptual project comes from my experiences during 19 years of teaching Information Technology.  During that time, I also served as Coordinator of my program for more than 10 years.  In my experience, the current method of advising students based on their own selection of a degree track program is not a very effective process.  More often than not, students get to the end of their degree program and still do not have a full understanding on what they want to do with the skills they have learned.  They have just taken the courses they were advised to take and do not know how those courses will lead them to a career they want to have.  

Another motivation is to help diversify the gender of students entering in the IT field.  The student population in IT is mostly male.  Female students only make up between 10-20% of students in our classes.  The averages are a bit higher for database and programming courses (25-27%) and lower for networking courses (13-15%).  Nationwide the percentage of female students hovers around 18%.  In order to increase the percentage of female students and diversify the student population, we need to look for new ways to attract students in to IT.   Using a more interest based, skills based advising system may appeal more to female students.  In addition, the flexibility of changing their learning paths will help them feel confident that if they do not like a particular path they will have the flexibility to change.

Literature Review

There are a number of studies available which look at the concept of developing a decision based, or object oriented based approach to student advisement.  Most of these sources concentrate on the methods or algorithms required to create an automated advisement system.  During my research I was not able to find an article which focused on the interface design of an advisement system.  However, there is some very valuable information in these articles and in the references cited in these articles.  This initial literature review can serve as a basis for further research on this topic.

Farzan and Brusilovsky (2006) present their findings in the development of CourseAgent, an adaptive community-based hypermedia system, which provides social navigation course recommendations based on student’s assessment of course relevance to their career goals.  CourseAgent obtains students’ explicit feedback as a part of their interactions with the system.  Farzan and Brusilovsky found that engaging students in a “do-it-for-yourself” type of approach was successfully in eliciting more course recommendations and observing progress toward their career goals is an important motivation to students while providing more explicit and implicit feedback to the system. 

O’Mahony and Smyth (2007) report on their work concerning the development of a course recommender system for the University College Dublin’s on-line enrolment system.  They outline factors that influence student choices including Interests and academic knowledge; Career goals; Module prerequisites and co-requisites; Ability to progress study; Difficulty and format of module; Awareness of options and Availability of places and timetable clashes.  This study is helpful to the development of this project as it helps to outline the types of information and criteria that should be considered in developing an advisement system.  This system uses an algorithm-based  “More Like This” recommender which uses course text fields such as title and learning outcomes to create comparisons to other similar courses.  This algorithm would be worth studying to see how it could be used to create matches for interests and skills. 

Werghi and Kamoun (2010) developed a Decision Support System (DDS) for student advising.  The system was designed to provide students with an automated program planning and scheduling service that best fits their profiles while meeting academic requirements.  The majority of this paper focused on the implementation of the decision tree and the calculations required to support the decision tree approach.  This paper would be worth studying to learn how the decision support system works and how it could be used to make course decisions based on various factors. 

In Al Ahmar (2011), the objective of the research was to develop a prototype student advising expert system that assists Information Systems (IS) majors in selecting their courses.  The system is based on an object oriented database and a rules based approach to decision making.  The expert system developed is capable of advising students using a prescriptive advising model and a developmental advising model.  The system uses a graphical user interface and simple menus.  Information is displayed in a way that is considered to be familiar to both the student and academic advisor.  System testing revealed that the system is capable of matching the results of human based advising.

Audience Analysis

The target audience for this system are students who are interested in pursuing a degree or certificate program in Information Technology (IT) at Santa Fe College.  These students range in ages from highschool dual enrolled, traditional college age, non-traditional college students returning back to college after military service, career changers, or late entry college students.  While the students have selfidentified as being interested in IT, they are not always knowledgeable enough about the options within IT to adequately evaluate the various degree and certificate options to choose a path that will allow them to be successful in a new IT career.  

One motivation for the development of this system would be to use it to advise students who don’t traditionally think of IT as a potential career path.  By focusing on interests and skills, rather than courses and technology, students will be able to see the correlation between their own interests and their application to how information technology can be used to influence and support our society.  This is particularly important in looking at increasing the gender diversity of the IT student population. Female students are more likely to be interested in IT career paths if they can see the link to how their participation will affect society and how they can use technology to accomplish their goals.

Conceptual Design

This system is designed to be a blend of a career advisement system and a course selection registration system.  The system advises students based on their interests, skills, learning preferences and course progression.  This is compared to the current system of advising students based on a selected degree track, course completion model.  The resulting system will recommend a variety of learning paths and allow students to choose the one they are most interested in.  

Each semester the student will be presented with the ability to rank their previous semester’s experiences and update their preferences. The advisement process is on-going and iterative in nature as the student learns more about the IT field and further refines their interest level or adapts their learning styles.  They may choose a new learning path, or continue down their current learning path as desired until they complete a path that leads them into a desired career.

The design of the system will be highly graphical in nature and will be designed to take advantage of a touch screen interface.  Screens will be simple, following the “Don’t make me think!” concept (Krug 2014). Selections will be made by dragging and dropping information into boxes or into the trash can.  Familiar icons and colors are used to indicate selection options.  Green for yes, Red for no, Yellow for caution or maybe.  Options which are not currently available will be grayed out.  A trash can will be available for discarding items students don’t want in their selection. Using colors and metaphors that will help students navigate the system. (Rogers, Sharp et al. 2011)  All informational or help elements will be revealed by hovering over, or by touching and holding either with a finger or stylus.

Product Requirements

This system will be designed to be a tablet based application.  It could be offered as a mobile web app, but would probably be best rendered on a device that provides a touch screen interface.  The drag and drop nature of the screens will function using a mouse, but would be more appropriate on a touch screen. 

For my product requirements outline, I used Mind Outliner by Aphalina.  The version I used is a Windows 8 app available through the Microsoft store.  

Adaptive Advisement System

  1. Welcome splash screen
  2. Getting Started  (only displayed on first use)
  3. On first use, students will be taken through a series of screens to gather interest data, prior learning, and current skill levels.
    1. Tell us about your interests: Students will be given a list of topics or statements to drag into an interest’s box. After each item is dropped into the box, the will be presented with a slider to drag right or left (+ or -) to indicate the strength of interest.
    2. Tell us about other skills you have: Students will be able to drag and drop a list of skills to build a portfolio of skills they feel will be beneficial to their learning path.  After each item is dropped into the box, the will be presented with a slider to drag right or left (+ or -) to indicate the strength of interest. 
    3. Tell us about your preferred learning styles and schedules: Students will be able to drag and drop descriptors which indicate their preferred learning styles, and availability. After each item is dropped into the box, the will be presented with a slider to drag right or left (+ or -) to indicate the strength of interest.
    4. Tell us about the courses you have completed: This system should pull from student transcripts.  Student chooses a ranking by moving a slider right/left to indicate whether or not this topic was something they wanted to pursue. Student should be instructed to evaluate the topic, not the class presentation or the instructor.  This value will be used to determine which subjects are moving student toward desired career goals.
    5. Start your journey- The learning path: Students will be presented with the beginning of a learning path.  Courses offered will be selected based on an algorithm that matches their interests, current skill levels, prerequisites met, preferred learning styles, etc.. The screen will also show possible completion paths with estimated time to completion, difficulty level, and potential payoff (job possibilities, salaries, etc.).  As this is a first time usage, students may be presented with more explanatory information on this visit to the learning path.  
    6. Registration: Students can drag and drop courses from their path to a registration box.  After all courses are dropped in they will be presented with a scheduler and options if there are multiple sections. They then click to highlight the ones they want and submit.  Choices are offered based on an algorithm that prefers sections that match their learning style and preferred schedules.
  4. Continue your journey.  (Returning students are given the opportunity to update their information before accessing their learning path.)
    1. You gave new courses!: Pop up notification. Required if new course completion on transcripts. Students are given the opportunity to rank courses they have completed since the previous session.  Course completion data is provided by student transcripts. Grades, and ranking data are recorded and added to decision algorithm data. 
    2. Update skills: Students are given the opportunity to update skills.
    3. Update learning preferences and schedules: Update interests
    4. Students are given an opportunity to update interests.
    5. The learning path: Students are presented with the beginning of a learning path.  Courses offered will be selected based on an algorithm that matches their interests, current skill levels, prerequisites met, preferred learning styles, etc.. The screen will also show possible completion paths with estimated time to completion, difficulty level, and potential payoff (job possibilities, salaries, etc.).  
    6. Scheduling: Students can drag and drop courses from their path to a schedule box.  After all courses are dropped in they will be presented with a scheduler and options if there are multiple sections. They then click to highlight the ones they want and submit.  Choices are offered based on an algorithm that prefers sections that match their learning style and preferred schedules.
  5. System maintenance.
    1. Interests
    2. Interests, task descriptions, etc. that students can use to describe the types of things they enjoy, or imagine they would enjoy. 
    3. Skills: Skills that students may have that are not necessarily validated as courses, or other measured learning elements.
    4. Learning styles: Schedule options and formats.
    5. Courses: Courses can include traditional courses, learning modules, or other competency based measures of prior learning.

Physical Design

For the physical design I created paper and colored pencil (sometimes crayon) wireframe sketches.  The sketches were then photographed and uploaded into POP (Prototyping on Paper) app which allows you to add hotspots and links to essentially create a prototype application.  I will bring my prototype application to class on a Windows 8 tablet share with anyone who is interested in viewing the application.

Below are the main screens I created for my application.  In addition, there are several pop up screens that would be used for mouse over or informational support screens.

Welcome: The introductory splash screen.  Uses a simple green “Start” arrow to guide the student to begin the program.
Tell us about Yourself screen image.
Tell us about yourself:  This is the first of the drag and drop screens.  The middle box will be a slightly grayed out box and will be populated with a variety of interest categories and/or statements.  Students will drag the boxes to the right, light green colored box to indicate interests which they identify with.  When an interest is dropped in the box, they will be presented with a slider to rank the strength of their identification with that interest.  Students can also drag interest categories to the trash can to indicate no level of interest.   Students can continue dragging and dropping interests until they feel they are satisfied with the pool of interests in the “My Interests” box.  From here, they can either save and exit, or click next to continue with the process.
Tell us about your skills: This is the second of the drag and drop screens.  The process here is the same as with interests, except now they are selecting and ranking skills.  I realize in reviewing this slide, I forgot to add a back button.  In future iterations, I will add a “Back” arrow appearing just to the left of the “Save and Exit” button.

Tell us about your learning preferences: This is the third of the drag and drop screens.  In this screen students are selecting and ranking learning preferences.  Examples could include items such as online learning, classroom based learning, hands-on learning, video demonstration, reading a textbook, writing a paper, researching on the internet, working independently, working in groups, etc. Again, I need to add a “Back” button here.
Tell us about the courses you have taken: This is the fourth of the drag and drop screens.  In this screen students will give feedback on the courses they have taken and how well they identified with these courses.  Courses they have taken will appear in the gray courses box.  When they drag them into the My Courses box, they will be presented with a slider to indicate their level of satisfaction with that course.  Again, if a student really did not like a course, they can drop it into the trash bucket.  I need to add a “Back” button to this page.
What is your availability? In this screen, students will indicate their availability for taking classes.  They will be presented with a schedule where they can click on cells and toggle the block between a red “x” for no, yellow “?” for maybe but not preferred, and a green “√” to indicate yes.  There should also be a place on this page to indicate yes or no for online courses.
Learning Paths: Here is where students are presented with an option to choose their learning path.  The idea here is an intersection visual representation of the learning paths the student can take based on their current progress through the program.  The beginning of each learning path begins at a place where the student has completed all prerequisites.  Displayed on the learning paths are ideas of the types of jobs, or careers the students could expect if they completed that learning path.  Also displayed should be an estimated time for completion for each level.
Recommended schedule: The final interactive screen is a recommended schedule.  Recommended courses will be offered.  Initially they will all be yellow, to indicate options.  Students can then toggle to yes or no, to indicate their preference.  All courses shown on the recommended schedule will be available to the student.  The system will only display courses where the student has successfully completed all course prerequisites.   Once a student toggles a particular section green, all other sections for that course will gray out.  All green course blocks will be added to the students course schedule.
Thank you for using the IT Advisement System.

Once the students has saved, and exited the program, they will be presented with a thank you screen and contact information for more assistance if needed.

References:

Al Ahmar, M. A. (2011). “A Prototype Student Advising Expert System Supported with an Object-

Oriented Database.” International Journal of Advanced Computer Science and Applications, Special Isue on Artificial Intelligence: 100 – 105.

Farzan, R. and P. Brusilovsky (2006). Social Navigation Support in a Course Recommendation System. Adaptive Hypermedia and Adaptive Web-Based Systems. V. P. Wade, H. Ashman and B. Smyth, Dublin, Ireland: 91-100.

Krug, S. (2014). Don’t make me think, revisited. Berkley, CA, New Riders.

O’Mahony, M. P. and B. Smyth (2007). A Recommender System for On-line Course Enrolment: An Initial Study. Proceedings of the 2007 ACM Conference on Recommender Systems, Association for Computing Machinery.

Rogers, Y., et al. (2011). Interaction Design. United Kingdom, John Wiley & Sons, Ltd.

Werghi, N. and F. Kamoun (2010). “A decision-tree-based system for student academic advising and planning in information systems programmes.” Int. J. Business Information Systems 5(1).

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