现代教育技术
現代教育技術
현대교육기술
Modern Educational Technology
2014年
3期
86~93
,共null页
mCSCL 学习伙伴 移动学习 KNN算法 动态分组
mCSCL 學習夥伴 移動學習 KNN算法 動態分組
mCSCL 학습화반 이동학습 KNN산법 동태분조
mCSCL; learning partner; M-Learning; KNN algorithm; dynamic grouping
随着信息技术的快速发展,mCSCL已成为教育技术学领域新的研究热点,学习伙伴选择合理与否将直接影响着协作学习效率.文章利用mCSCL环境下协作分组伙伴模型,提出了基于KNN的mCSCL学习伙伴分组理论,通过计算学习者之间的相似度和类别权重,提供一张可视化的学习伙伴关系图,导学者遵循组间同质和组内异质分组原则,为学习者动态推荐最佳学习伙伴;并设计了以小学一年级加减运算为内容的mCSCL活动,开展分组满意度访谈和小组学习效率实证研究.实验结果表明,相对于随机分组方式,基于KNN算法的mCSCL学习伙伴分组方式更适合移动学习活动开展,学习效率更高.
隨著信息技術的快速髮展,mCSCL已成為教育技術學領域新的研究熱點,學習夥伴選擇閤理與否將直接影響著協作學習效率.文章利用mCSCL環境下協作分組夥伴模型,提齣瞭基于KNN的mCSCL學習夥伴分組理論,通過計算學習者之間的相似度和類彆權重,提供一張可視化的學習夥伴關繫圖,導學者遵循組間同質和組內異質分組原則,為學習者動態推薦最佳學習夥伴;併設計瞭以小學一年級加減運算為內容的mCSCL活動,開展分組滿意度訪談和小組學習效率實證研究.實驗結果錶明,相對于隨機分組方式,基于KNN算法的mCSCL學習夥伴分組方式更適閤移動學習活動開展,學習效率更高.
수착신식기술적쾌속발전,mCSCL이성위교육기술학영역신적연구열점,학습화반선택합리여부장직접영향착협작학습효솔.문장이용mCSCL배경하협작분조화반모형,제출료기우KNN적mCSCL학습화반분조이론,통과계산학습자지간적상사도화유별권중,제공일장가시화적학습화반관계도,도학자준순조간동질화조내이질분조원칙,위학습자동태추천최가학습화반;병설계료이소학일년급가감운산위내용적mCSCL활동,개전분조만의도방담화소조학습효솔실증연구.실험결과표명,상대우수궤분조방식,기우KNN산법적mCSCL학습화반분조방식경괄합이동학습활동개전,학습효솔경고.
With the rapid development of information technology, mCSCL has become a new field of educational technology research focused, whether is reasonable selection of learning partner will direct impact the efficiency of learning. This article uses the collaboration partner grouping model in mCSCL environment and proposes a mCSCL learning partner grouping theory based on KNN algorithm, which can provide a Visual Learning Partnership map by computing the similarity between learners and classification weight. The scholars recommend the best learning partner for learner dynamically according to the grouping principles of homogeneous between group and heterogeneous in the group to. Paper designed a mCSCL activities about the first grade of addition and subtraction, developed the interview of the satisfaction about the grouping of learning partner and the empirical study about the learning group's study efficiency. Experimental results show that the way of mCSCL learning partner grouping based on KNN algorithm is more appropriate for mobile learning activities ,and learning more efficient relative to the random grouping.