Alhathli Manal, Masthoff Judith, Beacham Nigel
Department of Computing Science, University of Aberdeen, Aberdeen, United Kingdom.
Vice-Rectorate of Development and Quality, Princess Nourah Bint Abdul Rahman University, Riyadh, Saudi Arabia.
Front Artif Intell. 2020 Mar 24;3:11. doi: 10.3389/frai.2020.00011. eCollection 2020.
This paper investigates how humans adapt next learning activity selection (in particular the knowledge it assumes and the knowledge it teaches) to learner personality and competence to inspire an adaptive learning activity selection algorithm. First, the paper describes the investigation to produce validated materials for the main study, namely the creation and validation of learner competence statements. Next, through an empirical study, we investigate the impact on learning activity selection of learners' emotional stability and competence. Participants considered a fictional learner with a certain competence, emotional stability, recent and prior learning activities engaged in, and selected the next learning activity in terms of the knowledge it used and the knowledge it taught. Three algorithms were created to adapt the selection of learning activities' knowledge complexity to learners' personality and competence. Finally, we evaluated the algorithms through a study with teachers, resulting in an algorithm that selects learning activities with varying assumed and taught knowledge adapted to learner characteristics.
本文研究人类如何根据学习者的个性和能力来调整下一个学习活动的选择（特别是其假设的知识和传授的知识），以激发一种自适应学习活动选择算法。首先，本文描述了为主要研究生成经过验证的材料的调查，即学习者能力陈述的创建和验证。接下来，通过一项实证研究，我们调查了学习者的情绪稳定性和能力对学习活动选择的影响。参与者考虑一个具有一定能力、情绪稳定性、近期和先前参与的学习活动的虚构学习者，并根据其使用的知识和传授的知识选择下一个学习活动。创建了三种算法，以根据学习者的个性和能力调整学习活动知识复杂性的选择。最后，我们通过与教师的一项研究对算法进行了评估，得出了一种选择具有适应学习者特征的不同假设和传授知识的学习活动算法。
