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Related Concept Videos

Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
Machines: Problem Solving I01:22

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb,...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Related Experiment Videos

CoDeRS: a computing degree program recommender system using machine learning algorithms.

Christian Alexandra Rances1, Rassel Aviel Sipe1, Sherwin Baris1

  • 1College of Computer Studies and Systems, University of the East, Manila, Philippines.

Frontiers in Artificial Intelligence
|July 16, 2026
PubMed
Summary

A new web-based recommender system, CoDeRS, helps students find suitable computing degrees by analyzing interests and personality. The system demonstrated high usability and recommendation acceptance, proving effective for career guidance.

Keywords:
IT educationalgorithmartificial intelligencedegree programrecommendation system

Related Experiment Videos

Area of Science:

  • Educational Technology
  • Computer Science
  • Human-Computer Interaction

Background:

  • Students often struggle to select appropriate computing degree programs.
  • Existing guidance systems may not fully integrate personal and academic factors.

Purpose of the Study:

  • To develop and evaluate CoDeRS, a web-based recommender system for computing degree selection.
  • To assess the system's usability, reliability, user experience, and overall satisfaction among students.

Main Methods:

  • A six-stage development process incorporating user profiles, academic data, and personality types (Big Five Inventory).
  • Multinomial Naïve Bayes for user profiles and content-based filtering for academic data.
  • Cosine similarity for personality matching, with a recommendation engine trained on 352 computing students.

Main Results:

  • High usability ratings: 85.7% found it easy to navigate, 81% easy to use.
  • Recommendation acceptance rate of 76.2%, with over 50% intending future use.
  • Performance metrics: 81.6% accuracy, 84.2% recall, and 77.9% precision.

Conclusions:

  • CoDeRS is an effective technology for aiding students in making informed computing degree choices.
  • The system successfully integrates diverse data points to provide tailored recommendations.
  • Further development and application of CoDeRS are warranted for educational guidance.