Scaradozzi David, Cesaretti Lorenzo, Screpanti Laura, Mangina Eleni
Dipartimento di Ingegneria dell'Informazione (DII), Università Politecnica delle Marche, Ancona, Italy.
LSIS - umr CNRS 6168, Laboratoire des Sciences de l'Information et des Systèmes, Equipe I&M (ESIL), Marseille, France.
Front Robot AI. 2020 Mar 13;7:21. doi: 10.3389/frobt.2020.00021. eCollection 2020.
This paper presents the design of an assessment process and its outcomes to investigate the impact of Educational Robotics activities on students' learning. Through data analytics techniques, the authors will explore the activities' output from a pedagogical and quantitative point of view. Sensors are utilized in the context of an Educational Robotics activity to obtain a more effective robot-environment interaction. Pupils work on specific exercises to make their robot smarter and to carry out more complex and inspirational projects: the integration of sensors on a robotic prototype is crucial, and learners have to comprehend how to use them. In the presented study, the potential of Educational Data Mining is used to investigate how a group of primary and secondary school students, using visual programming (Lego Mindstorms EV3 Education software), design programming sequences while they are solving an exercise related to an ultrasonic sensor mounted on their robotic artifact. For this purpose, a tracking system has been designed so that every programming attempt performed by students' teams is registered on a log file and stored in an SD card installed in the Lego Mindstorms EV3 brick. These log files are then analyzed using machine learning techniques (k-means clustering) in order to extract different patterns in the creation of the sequences and extract various problem-solving pathways performed by students. The difference between problem-solving pathways with respect to an indicator of early achievement is studied.
本文介绍了一种评估过程及其结果的设计，以调查教育机器人活动对学生学习的影响。通过数据分析技术，作者将从教学和定量的角度探索这些活动的产出。在教育机器人活动中使用传感器，以获得更有效的机器人与环境的交互。学生们通过完成特定的练习，使他们的机器人更智能，并开展更复杂、更具启发性的项目：在机器人原型上集成传感器至关重要，学习者必须理解如何使用它们。在本研究中，利用教育数据挖掘的潜力，来调查一群中小学生在使用可视化编程（乐高Mindstorms EV3教育软件）解决与安装在其机器人制品上的超声波传感器相关的练习时，是如何设计编程序列的。为此，设计了一个跟踪系统，以便学生团队进行的每次编程尝试都记录在日志文件中，并存储在安装在乐高Mindstorms EV3积木中的SD卡上。然后使用机器学习技术（k均值聚类）对这些日志文件进行分析，以提取序列创建中的不同模式，并提取学生执行的各种解决问题的途径。研究了关于早期成就指标的解决问题途径之间的差异。
