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Innovative Flipped Pedagogy for Statistical Methods

Learn about the development of a flipped pedagogy for teaching statistical methods to engineers, resulting in increased student engagement, improved evaluations, and reduced failure rates.

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Innovative Flipped Pedagogy for Statistical Methods

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  1. StatisticalMethods for EngineersDeveloping a FlippedPedagogy15 yearsbeforeits inventionMarc BourdeauProfessorEmeritusPolytechniqueMontréale-Cots, May 2014(For the slides, seeWikiStat.ca: last line of the page)

  2. Everyterm 3 to 4 sections of around 60 students A coordinatingprofessor Otherprofessors One TA for each group

  3. Weeklytheoreticalworkload(4; 1; 4)(Lectures, Labs, Personalstudy)…In practice(1,5; 0,25; 1) One mid-term, one final; Fail rate: 20% (unacceptable for our administration) Decreasinglevel of the course over the years 2 homeworks (teams of 4) widelycopied; grade inflation

  4. In brief, at the turn of the century Students show no interest …as well as the staff! Dying course «Hopeless !»

  5. New pedagogy for the course «Reading maketh a full man;conference, a ready man;writing, an exact man.»(Of Studies, Francis Bacon [1561-1626]) Real data, team work, project based learning wall to wall work ‘within’ the software Simulating the work in an industrial context

  6. A website For the students to get information on the course and express themselves theirowngrades, extra documents, theirownevaluations of the course, etc.

  7. Studentevaluation of the courseCrucial Innovationthe pedagogical relation isbased on confidence Mostly open questions Closed questions on the student’sworkload

  8. Rigorous Protocol Theevaluationformisgiven to the studentswiththeirmarkedmid-term copies Whichthey hand in to 2 ‘reporters’, fellow class mates, who count them put theirinitials on the page, and givetheirreportwith the evaluationforms to the professor The professor compiles the students’ evaluations, reports to the students at the following meeting of the class, givesthem back to the studentswhocanannotate, comment, discussthem The wholeprocessagainduring the last period of the course

  9. Developing a ‘new’ pedagogy Reading A new textbook Only one adapted to ourneeds: Ostle&al. (1996) «Engineering Statistics. The industrialexperience.» Termafterterm, I write, rewrite a new one (Pdf-Latex) withhyperlinks, historical notes, animations, simulations, lots of simple exercices

  10. Professors and TAsless and less sages on the stagemore and more guides in the aisles Beginning of the course: lectures on the elements, then an experiment in data collecting: variation All along the course:less and less lectures The professorisless and less a ‘sage on the stage’ For the students, more and more: readingthe textbook, writing and discussing simple problems (labs) with team mates Homeworks are case studiesthatintroduce new material Report writingwithteam mates Students attend classes to discusstheirworkwith the professor and fellow class mates, ask questions Reading, meeting & writing

  11. 10 labs, 20-25% of the final grade Teams of 2, both team mates hand in theircopy 10-12 ‘short’ exercices: weekly suggestions At the start of the lab: 3 «are chosen at random»: writing One, «randomlychosen», iscarefullymarked The staff and the TAs serve as guides Reading, meeting & writing

  12. 2 case studies (35%) The closestwecanget to the type of workpractised In the industry 15-20 pages of definition: statisticalquestioningand reasoning Teams of 4 20-30h fromeach team-mate: study/report writing,approx. 20 pages: hard team-work Students come in class for discussion, questions (they all come!) Reading, meeting, writing

  13. Course validation (40%) A mid-term exam 15% (2h) A final exam 25% (2h30) Individualwork Reading & writing

  14. Post-hoc situation Course ‘triplet’: (4; 1; 4) Situation ante: (1,5; 0,25; 1,0) post: (3,5; 1 ; 4) ‘Assist’: class attendance(noted 4) ‘Trav_P’ : personalstudy (noted 4) Trav_Tot’: Total workload Failure rate: close to 0% All the students attend the labs (worth 25%...) Students (not all) are enthousiastic: «We are learningsomethingusefull.» The staff (not all) are enthousiastic: «We are proud of ourstudents, feeling of usefulness»

  15. Drawbacks 1 ‘Large’ workload for the students …old habits ‘Huge’ workload for the staff …old habits!

  16. Drawbacks 2 Reallytoomuchwork for the professors Lack of institutionalresources ($$) The case studiescannotbereusednorrecycled: the students have theirownwebsites! The ‘other’ workload of professors (research, etc.) Annoyanceswith …certain students (verytenaciousand wellorganised), …certain colleagues (verytenacious and …envious), …certain administrators (verytenacious, side in withstudents) « I don’twant no bothering »

  17. All in all We have shownthat The students are not stupid, on the contrary! Manycanbe made to beenthousiastic for Statisistics Pedagogyis crucial Reading, meeting, writing; decentevaluation No cheating, no grade inflation, increasinglevels… Professorscanbeproud of theirstudents! More resources have shouldbegiven to the teachingsector in colleges and universities

  18. PrixPoly1873 for outstandingmerit (2001)For pedagogicalefficiency This experiment has lasted 5 terms, 1999-2001 Sabbaticalleave… Mysuccessorwentback to ouroldtextbook, old slides …oldways, sameeffect Offeredetching: «L’oiseau rare»

  19. Somereferences Recent and modern textbooks Somearticles

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