運用群眾外包之學生課堂專注度分析研究

Abstract

學生在課堂上的專注程度對於他們在學業成績方面的表現有著重要的影響力。當學生開始打瞌睡，他們的學習效率就會開始降低，透過評量分析學生專注度的程度，我們可以改善學生在課後的復習效率，並給予講師一些演講的意見，讓下次的演說更趨於完美。目前的精神狀態評量方式有著高成本、儀器部署的限制和需要配戴的問題，像是使用影像處理或是瞌睡警示器。僱用專家來對學生精神狀態做評量分析容易產生因疲憊和過於主觀使得評量結果不準確、不客觀等問題。在本研究中，我們介紹Concentration Level Assessment System (CLAS)，一個易於部署的精神狀態評量系統，透過Doze-and-Wake Model (DAWM)和model-driven的群眾外包方式，取得各種領域工作人員的貢獻來提供高準確度的學生專注度評量結果，並找出評量作業的成本和評量準確度的平衡點。由於CLAS是一個輕巧、準確度高且低成本的精神狀態評量系統，因此能用於其他場合使用，未來我們也希望使用在辦公室內對於員工的精神狀態做監測，方便公司管理人對於下屬的管理。

關鍵字：學生、精神狀態、瞌睡、群眾外包

Concentration is important for students to conduct efficient learning in a class, and an effective assessment of students’ concentration level in a class is useful for students to review class materials after lectures, as well as for lecturers to adjust their teaching strategies for self-improvement. Although a number of concentration assessment approaches have been proposed and deployed, these approaches are generally inaccurate (e.g., computer vision-based approaches), intrusive (e.g., wearable anti-doze alarm devices), and time/money expensive (e.g., expert assessment). In this study, we propose a novel approach, called Concentration Level Assessment System (CLAS), which combines a markovian Doze-and-Wake Model (DAWM) and emerging crowdsourcing marketplace to provide the concentration assessment service. Using a comprehensive set of real-world experiments and extensive data analysis, we demonstrate that the proposed system is capable of striking a good balance between assessment accuracy and monetary expense. Moreover, CLAS is simple, accurate, affordable, and promising in facilitating concentration assessment in different application scenarios.

Key words:students, concentration, doze, crowdsourcing