教学交互是cMOOC学习者取得学习成功的关键.研究cMOOC学习者教学交互转化的特征并探究教学交互转化的演化过程及其规律,对进一步认识cMOOC学习者的学习过程具有重要意义.本研究聚焦cMOOC课程"互联网+教育:理论与实践的对话"第二期模块四的学习者,依据联通主义学习教学交互与参与模型对学习者产生的文本内容进行编码,从整体学习阶段和5个具体学习阶段分别对编码数据进行滞后序列分析,从序列的视角分析了cMOOC学习者教学交互转化的特征,从动态视角探讨了教学交互转化的演化过程及其特征,以期为进一步揭示联通主义学习的基本规律提供实证依据.研究发现,cMOOC学习者的教学交互转化具有两大特征:层次内大于层次间、上层扩展多于下层支撑、层次间渐进;寻径交互层次内的交互转化与意会交互层次内的交互转化在数量分布与转化方式上存在差异.cMOOC学习者教学交互转化的演化具有三大特征:总体上具有话题周期性的特征,主要体现在同一交互层次内;不同交互层次间交互转化的演化总体上呈现出一致性,而意会交互与创生交互之间交互转化的演化具有特殊性;产生不同层次教学交互的交互转化之间具有层次间相异、层次内交叠的演化特征.

Teacher training is significant for improving the quality of teachers and education in deep poverty areas. This study aims to investigate how the national teacher training projects supported the improvement of teacher professional development in deep poverty areas in China. Participants were trainees of national teacher training programs in 2018 and 2019 in deep-poverty areas in China, and a total of 1240 valid questionnaires were collected. This study finds that: 1) teacher training in the deep poverty areas of China has improved overall in making up for shortcomings; 2) the low outcome of skill and ability is the prominent shortcoming of teacher training in the deep poverty areas of China; 3) the overall outcome of teacher training in deep poverty areas of China is lower than the national average level, and the gap is widening.

随着我国教育信息化进入2.0阶段,要求教师具备信息技术与教学深度融合创新的能力.近年来,国家重视对教师信息化教学能力的培训,采取了不少政策举措.为深入了解我国现阶段国培项目促进教师信息化教学能力提升的进展情况,研究采用分层抽样的方法对2018年、2019年来自全国31个省市自治区的国培项目参训教师的信息化教学能力培训成效进行了调查.通过对两年国培促进信息化教学能力成效产出情况的年度比较,深入描绘和挖掘了国培项目促进教师信息化教学能力提升的进展和困境,并基于研究结果提出了三个建议,希望能够为以国培项目为代表的教师培训,以及我国中小学教师信息技术应用能力提升2.0工程提供参考.

我国高度重视教师培训工作,国培项目已进入"提质增效"阶段.为深入了解我国教师培训助力教师专业成长的成效,有针对性地指导未来教师培训工作,研究采用实证研究范式,连续两年对国培项目的培训成效进行调查,并基于年度比较研究分析国培项目助力教师专业发展是否提质增效.研究结果显示:国培项目助力教师专业发展,实现全方位、立体化提质增效;助力教育公平,西部地区提质增效显著,而新"中部低洼"现象突显;助力乡村教师专业发展提质增效显著,然城乡差距加大;培训模式优化创新初见成效,然校本培训发展滞后.针对存在的问题,该文提出了下一阶段我国教师培训由外延式发展向内涵式发展转变的四个建议.

As a type of MOOC based on Connectivism, cMOOCs raise a special model of teaching, tutoring, and learning. Hence, compared with the xMOOCs, cMOOCs would need particular approaches to better evaluate the teaching and learning of the connectivist teacher and learners. This study aims to explore a new way to evaluate a cMOOC course by analyzing and comparing the epistemic networks of the teacher and learners, including comparing their epistemic network structure and their development trajectory. Based on a cMOOC course offered by Beijing Normal University in the spring of 2019, 1068 pieces of interactive text data of learners and 40 pieces of interactive text data of the teacher were collected. Methods of Epistemic Network Analysis (ENA), Cluster Analysis, and Content Analysis were used. The study found that there were some differences in the cognitive structure between the teacher and learners in the whole learning process, but no statistically significant difference. Learners' cognition was hysteretic and autonomous. Teachers should also play tutoring and facilitating roles on learners especially in the middle phase of the course. cMOOC was found to be feasible and effective to use ENA to evaluate connectivist learning.

Connectivist learning is gradually becoming the main online learning form. Interaction is the foundation, process, and result of connectivist learning. It is the key to success in learning. This study aims to explore the networked relationships and action mechanism among interactions. Methods of content analysis and social network analysis are used to analyze the text data generated by learners in Module 4 of the first Chinese cMOOC course and the following conclusions are obtained. Innovation interaction plays an important role in the formation of the interaction network mainly playing the role of outward expansion. Sensemaking interaction occupies the position of the broke and assumes the role of bridging Wayfinding interaction and Innovation interaction. Wayfinding interaction is located at the edge of the network and it plays a role in promoting the generation of Sensemaking. In addition, there is a certain recursion between the three-type interactions reflected in their structural similarity. This study provides support for research related to connectivist interaction and provide guidance for teachers to design, implement, and optimize the connectivist courses.

Rui Wang,1,2* Jie Wang,1* Jun-Yun Zhang,2* Xin-Yu Xie,2* Yu-Xiang Yang,2 Shu-Han Luo,1 Cong Yu,2 and Wu Li1 1State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China, and 2Department of Psychology, IDG/McGovern Institute for Brain Research, and Peking-Tsinghua Center for Life Sciences, Peking University 100871 Beijing, China

Humans can learn to abstract and conceptualize the shared visual features defining an object category in object learning. Therefore, learning is generalizable to transformations of familiar objects and even to new objects that differ in other physical properties. In contrast, visual perceptual learning (VPL), improvement in discriminating fine differences of a basic visual feature through training, is commonly regarded as specific and low-level learning because the improvement often disappears when the trained stimulus is simply relocated or rotated in the visual field. Such location and orientation specificity is taken as evidence for neural plasticity in primary visual cortex (V1) or improved readout of V1 signals. However, new training methods have shown complete VPL transfer across stimulus locations and orientations, suggesting the involvement of high-level cognitive processes. Here we report that VPL bears similar properties of object learning. Specifically, we found that orientation discrimination learning is completely transferrable between luminance gratings initially encoded in V1 and bilaterally symmetric dot patterns encoded in higher visual cortex. Similarly, motion direction discrimination learning is transferable between first- and second-order motion signals. These results suggest that VPL can take place at a conceptual level and generalize to stimuli with different physical properties. Our findings thus reconcile perceptual and object learning into a unified framework.SIGNIFICANCE STATEMENT:Training in object recognition can produce a learning effect that is applicable to new viewing conditions or even to new objects with different physical properties. However, perceptual learning has long been regarded as a low-level form of learning because of its specificity to the trained stimulus conditions. Here we demonstrate with new training tactics that visual perceptual learning is completely transferrable between distinct physical stimuli. This finding indicates that perceptual learning also operates at a conceptual level in a stimulus-invariant manner.

It is assumed that the brain relies on orientation detectors in the early visual cortex for orientation discrimination. Accordingly, perceptual learning of orientation discrimination is often interpreted as refined readout of inputs from these orientation detectors at a later decision stage. However, recent studies demonstrated that orientation learning can transfer completely across the hemispheres and to an orthogonal orientation, suggesting that orientation learning could occur in high-order brain areas. Therefore, there exists the possibility that, instead of direct readout of inputs from the orientation detectors, the high-level decision stage may rely on orientation inputs from later stages of brain processing. To test this hypothesis, we trained human observers to discriminate the explicit orientation of gratings, putatively detected by V1 neurons; or the implicit orientation of mirror-imaged dot patterns with a single axis of symmetry, unlikely detectable in V1 but most likely encoded by higher cortical areas. We compared the mutual transfer of learning between these two distinct stimuli. Learning in orientation discrimination of the symmetric dot patterns transferred completely to the gratings. In contrast, learning in orientation discrimination of the gratings transferred only partially to the dot patterns; but subsequent exposure to the same-oriented dot patterns in a suprathreshold irrelevant task ("which of the two patterns contains more dots?") (a Training-Plus-Exposure technique, Zhang et al., J. Neuroscience, 2010) further markedly reduced orientation thresholds for the dot patterns, achieving a complete learning transfer from the gratings to the dot patterns. The complete learning transfer, especially from the implicit orientation of symmetric axis to explicit orientation of gratings, is unpredicted if orientation discrimination and its learning rely on direct readout of inputs from early orientation detectors. Our findings pose the challenge of understanding the neural computations for fine orientation discrimination and its further refinement through perceptual learning in later cortical processing. Meeting abstract presented at VSS 2012