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An alternator converts mechanical energy into electrical energy that varies sinusoidally, resulting in AC current. Meanwhile, a DC generator converts mechanical energy into electrical energy, which are DC pulses with the same polarity. The construction of a DC generator is similar to that of an alternator, except that the pair of slip rings is replaced by a single split ring, also called a commutator. The commutator functions like a periodic rotary switch; it changes the contacts with the...
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A three-phase AC generator has a rotor with a rotating magnet placed within the stator mounted with the stationary three-phase winding to generate three-phase voltages via mutual induction. These windings are evenly distributed around the inner circumference of the stator and are arranged 120 electrical degrees apart. Three-phase stator windings consist of three separate coils or groups of coils, known as phases, each connected in Y (star) configuration or Delta configuration.
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Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand, use...
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How do machine-generated questions compare to human-generated questions?

Lishan Zhang1, Kurt VanLehn1

  • 1School of Computing, Informatics, Decision System Engineering, Arizona State University, Tempe, AZ USA.

Research and Practice in Technology Enhanced Learning
|January 8, 2019
PubMed
Summary
This summary is machine-generated.

Two algorithms were developed to generate biology exam questions from knowledge bases and the web. All question types showed similar pedagogical benefits, though knowledge-based questions may be shallower.

Keywords:
Atomic PredicateBiology StudentKnowledge BaseQuestion SchemaSeed Pair

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Area of Science:

  • Biology Education
  • Educational Technology

Background:

  • Science instructors require a consistent supply of questions for various educational activities.
  • Existing textbooks often lack sufficient questions, necessitating external sourcing or question generation.

Purpose of the Study:

  • To develop and evaluate algorithms for generating questions for introductory college biology courses.
  • To compare the pedagogical effectiveness of algorithmically generated questions with textbook questions.

Main Methods:

  • Developed two algorithms: one generating questions from a photosynthesis knowledge base, another collecting questions from the web.
  • Compared generated questions with textbook questions.
  • Evaluated questions based on relevance, fluency, ambiguity, pedagogy, and depth using student and author ratings.

Main Results:

  • Little difference was found in the pedagogical benefits across question types (knowledge-based, web-collected, textbook).
  • Questions generated from the knowledge base were perceived as potentially shallower than professionally written textbook questions.
  • All three question sources demonstrated comparable effectiveness in aiding student learning.

Conclusions:

  • Algorithmically generated biology questions can serve as a viable supplement to traditional resources.
  • The study suggests that diverse question sources can equally support student learning in introductory biology.