Motivationale und affektive Merkmale unterschätzter Schüler

In this contribution, two investigations on teachers&rsquo; diagnostic competence and the motivational-affective characteristics of underestimated and overestimated students are presented. Each time, eight elementary school classes and their mathematics teachers participated in investigations in Germany and China. The fourth graders worked on a mathematics achievement test and a self-description questionnaire. Teachers estimated students&rsquo; test performance as well as motivational-affective characteristics. The distinction between underestimated and overestimated students was made on basis of the estimated and the real test&nbsp;performance. Both investigations have concurrent results. Teachers could predict test achievement with good accuracy, but had difficulties to assess students&rsquo; motivational-affective characteristics. Underestimated students showed the same test performance and learning motivation as overestimated students, but had lower expectancy for success, lower self-concept of ability and higher test anxiety. Teachers expected that underestimated students would get worse grades on the next mathematics test and would be satisfied with lower grades than overestimated students. Teachers&rsquo;judgment error did not confine to test performance but generalized to students&rsquo; motivational-affective characteristics.</p

Motivational and affective characteristics of underestimated students

In this contribution, two investigations on teachers&rsquo; diagnostic competence and the motivational-affective characteristics of underestimated and overestimated students are presented. Each time, eight elementary school classes and their mathematics teachers participated in investigations in Germany and China. The fourth graders worked on a mathematics achievement test and a self-description questionnaire. Teachers estimated students&rsquo; test performance as well as motivational-affective characteristics. The distinction between underestimated and overestimated students was made on basis of the estimated and the real test&nbsp;performance. Both investigations have concurrent results. Teachers could predict test achievement with good accuracy, but had difficulties to assess students&rsquo; motivational-affective characteristics. Underestimated students showed the same test performance and learning motivation as overestimated students,but had lower expectancy for success, lower self-concept of ability and higher test anxiety. Teachers expected that underestimated students would get worse grades on the next mathematics test and would be satisfied with lower grades than overestimated students. Teachers&rsquo;judgment error did not confine to test performance but generalized to students&rsquo; motivational-affective characteristics

Educational data mining from action LOG files of intelligent remote laboratory with embedded simulations in physics teaching

Remote laboratories enter the teaching process, especially in research based education. In connection of this, new demands on both teacher and student occur. Especially acute is the need for a fast feedback about the process of measurements and its correctness. All these is enabled by the static LOG file, providing a time record of all steps, executed during measurements. We present the detailed case study of remote laboratory “Transient phenomena in electric oscillations” with about 170 university students of bachelor studies. We discovered the “knowledge barrier” at the beginning of process of measurements, leading to the excessive time losses and discovering the sources of it in simple mathematical relation of formula—graph and inability to evaluate data accordingly. We also found the ill effect of this inability on the total time of laboratory exercise. As a solution we suggest the use of components of virtual and augmented reality and artificial intelligence for reasonable influence of students’ activities and advice together with cooperating experienced teacher. © 2019, Springer Nature Switzerland AG.Swiss National Science Foundation (SNSF) -"SCOPES"Swiss National Science Foundation (SNSF); Internal Agency Grant of the Tomas Bata University in Zlin, Czech Republi