![]() In addition to the author’s own course-specific survey, both institutions evaluated the courses by survey at the end of the semester. Some of the results from this survey is presented in Section 6 of the article. The response rate for the survey was 70 and 50 percent. The main closing survey was created in an online tool (SurveyXact) and was distributed to the students through the learning management system. The introductory survey was conducted online (SurveyXact was used) during class, and the purpose of the survey was both to generate data that would be used in examples and exercised throughout the course, but the author also introduced a limited set of questions related to the student’s access to study groups, how far away from the institution the lived, how they travelled to school, etc. An interview guide consisting of the main topics to be covered was prepared, but the author allowed the representatives to bring up new topics whenever this seemed relevant. The interviews were scheduled for 1–1.5 h, and were semi-structured. The methods used to gather data were in-depth interviews with student representatives (three, as the student group from institution B had two representatives) approximately midway in the semester, an simple introductory survey and a survey distributed to all students at the end of the semester. As discussed in more detail in Section 4, the primary goal was to increase students’ social engagement with the subject matter, and less focus has been placed on individual student activity. In order to examine if Padlet and other digital tools (explained in more detail in Section 5) could increase engagement with the subject matter-with an emphasis on social mediation-and consequently lead to less drop-out and better student performance, the intervention described in this article was implemented. However, while studies find that high levels of activity is sometimes correlated with higher student performance, in some cases it is found that extremely high activity is correlated with low performance, as it can result from a lack of understanding of the subject matter. The opposite-low levels of activity-is an indicator of high risk of drop-out. High levels of engagement and student activity is often considered to be an important indicator of effective learning, also in relatively technical subjects such as statistics. This tends not to be particularly effective for fostering student engagement and high levels of student activity. The two courses have traditionally emphasized traditional lectures with relatively little facilitation of between-lecture activity by the institutions. One possible explanation for the challenges experienced in these courses is that the students are taught statistics in relatively large groups in auditoriums. The students used Padlet actively, and a majority of the students reported that it was a significant or highly significant factor in their learning process. ![]() The results show that computer-supported collaborative learning can be an effective supplement or alternative to traditional study groups for those that either prefer this or cannot take part in regular study groups. The author’s experiences and the students’ evaluations of the model is presented here, along with the theoretical justification of the use of Padlet and the context in which it was tested. The purpose was to overcome the difficulty of engendering social engagement and activity and fostering effective mediation, scaffolding and collaborative learning in large student groups scheduled for traditional lectures in large auditoriums. ![]() In order to support the learning process in statistics courses, and in order to make the courses more flexible, the author experimented with the use of Padlet in two different student groups taking an introductory course in statistics for economists. Students often perceive statistics as a difficult subject, and it is frequently named as one of the primary causes of high dropout rates in economics educations in Norway.
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