Uncovering flipped-classroom problems at an engineering course on systems architecture through data-driven learning design Articles uri icon

publication date

  • May 2018

start page

  • 865

end page

  • 878


  • 3


  • 34

International Standard Serial Number (ISSN)

  • 0949-149X


  • Flipped classroom is a student-centered methodology that can help engineering students to acquire the cross-curricular skills demanded by society. However, its effectiveness relies on the commitment of both instructors and students. In particular, this strategy requires students to work on a number of proposed activities before face-to-face classes. Then, in order to follow the most appropriate path in those classes, instructors need a reliable way to know at which degree their students worked on those proposed activities, what issues they encountered while doing them and which concepts need to be reinforced in class. This paper presents a case study of a flipped-classroom undergraduate engineering course. By using data-driven learning design and learning analytics techniques we show that: (1) by delaying their work on the course activities our students actually drove the course towards the traditional approach; (2) despite directly asking students at the beginning of a face-to-face class might seem to be an appropriate way of getting reliable information about their previous work, it may lead instructors to erroneous conclusions; (3) our students were strongly mark- and deadline-oriented, but even a small grade encouraged them to work on the assignments; (4) the gathering and checking of students' learning data before the class can help instructors to tailor the lesson design; and (5) if students did not work on pre-class activities, dedicating a small amount of time of the in-class lesson to explain the most difficult concepts can help students to be more efficient with their work, at the cost of losing some of the spirit of the flipped classroom.


  • Telecommunications


  • flipped learning; engineering education; learning analytics; learning design; challenges