对中长跑教学的初探

中长跑教学在大学的体育教学中占有一定的比例,但是中长跑是一项比较难教的项目,其难点是：动作单一,趣味性不强,学生易感枯燥,单调而不愿学;再,长跑运动负荷大,学生在练习中怕“极点”的出现．针对这个问题,本人进行心理征的研究分析,采用了斯的教学手段,效果较好．     

Training boys with ADHD to work collaboratively: Social and learning outcomes

This study examined social participation and strategic problem solving behavior of boys diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) when collaborating on a planning task with a trained peer partner. Twenty-four 9- to 13-year-old boys with ADHD who were receiving a medication intervention, were individually pre-tested to assess their initial ability to plan an errand route task. They were then observed doing an alternate planning task during collaborative sessions with a female peer partner who had received prior training in the task and in facilitating social interaction. Boys with ADHD were then individually post-tested on the original planning task. Observations of the collaborative sessions revealed significant, positive changes across three phases in the quality of social interactions by boys with ADHD, in their planning strategies, and planning efficiency. Findings suggest positive benefits of collaborative learning structures for boys with ADHD when they are placed in a collaborative learning setting with a partner who has received a prior training intervention.     

教学视频中线索类型与学习者先前知识经验对学习的交互影响

教学视频因其多通道展现教学内容的特性而成为数字化学习资源的首选形态。线索作为教学视频中的重要引导性教学设计手段,探究其类型及学习者先前知识经验对学习效果的交互影响,有益于优化教学视频的设计策略。基于学习者先前知识经验水平(高水平和低水平)和线索类型(言语线索和视觉线索)两个维度,运用眼动追踪技术和认知负荷、学习满意度、学习效果等测量工具,通过实验考察二者对学习的影响及其内在机制后发现:学习者先前知识经验水平对认知负荷的影响显著;线索类型对学习满意度的影响显著,且线索类型与学习者先前知识经验水平在学习满意度上的交互效应显著;学习者先前知识经验水平对学习效果的影响显著,且线索类型与学习者先前知识经验水平在迁移测验成绩上的交互效应显著。实验结果表明,线索类型与学习者先前知识经验水平对学习满意度和学习效果存在明显的交互作用,即只有低知识经验水平学习者在学习含有视觉线索的教学视频后,学习满意度与学习效果才有显著提升。这可能是由于该类学习者在信息选择时存在困难,视觉线索可以帮助其提高信息搜索效率,而言语线索却会使其认知负荷超载。     

Dynamic intent-aware iterative denoising network for session-based recommendation

Session-based recommendation aims to predict items that a user will interact with based on historical behaviors in anonymous sessions. It has long faced two challenges: (1) the dynamic change of user intents which makes user preferences towards items change over time; (2) the uncertainty of user behaviors which adds noise to hinder precise preference learning. They jointly preclude recommender system from capturing real intents of users. Existing methods have not properly solved these problems since they either ignore many useful factors like the temporal information when building item embeddings, or do not explicitly filter out noisy clicks in sessions. To tackle above issues, we propose a novel Dynamic Intent-aware Iterative Denoising Network (DIDN) for session-based recommendation. Specifically, to model the dynamic intents of users, we present a dynamic intent-aware module that incorporates item-aware, user-aware and temporal-aware information to learn dynamic item embeddings. A novel iterative denoising module is then devised to explicitly filter out noisy clicks within a session. In addition, we mine collaborative information to further enrich the session semantics. Extensive experimental results on three real-world datasets demonstrate the effectiveness of the proposed DIDN. Specifically, DIDN obtains improvements over the best baselines by 1.66%, 1.75%, and 7.76% in terms of P@20 and 1.70%, 2.20%, and 10.48% in terms of MRR@20 on all datasets.     

TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI

Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve human living quality across the lifespan, which enables assistance of movement and communication, rehabilitation of exercise and nerves, monitoring sleep quality, fatigue and emotion. Most BCI systems are based on motor imagery electroencephalogram (MI-EEG) due to its advantages of sensory organs affection, operation at free will and etc. However, MI-EEG classification, a core problem in BCI systems, suffers from two critical challenges: the EEG signal’s temporal non-stationarity and the nonuniform information distribution over different electrode channels. To address these two challenges, this paper proposes TCACNet, a temporal and channel attention convolutional network for MI-EEG classification. TCACNet leverages a novel attention mechanism module and a well-designed network architecture to process the EEG signals. The former enables the TCACNet to pay more attention to signals of task-related time slices and electrode channels, supporting the latter to make accurate classification decisions. We compare the proposed TCACNet with other state-of-the-art deep learning baselines on two open source EEG datasets. Experimental results show that TCACNet achieves 11.4% and 7.9% classification accuracy improvement on two datasets respectively. Additionally, TCACNet achieves the same accuracy as other baselines with about 50% less training data. In terms of classification accuracy and data efficiency, the superiority of the TCACNet over advanced baselines demonstrates its practical value for BCI systems.     

注意力经济背景下的微博营销

注意力经济已经成为经济全球化过程中的显著特征,并且得到了学界的广泛认可.微博虽然是众多网络传媒工具中的后起之秀,但其暴风骤雨般的发展势头不容小觑.详尽解析了注意力经济和微博的概念及核心特征,确定了二者在营销层面的应用契合点.理论和实例并举,着重探讨务实可行的实施策略.     

高校实验教学改进方案探讨

专业课教学是传授学生专业技能和提高学生专业能力与素质的主要途径,长期的专业教学为社会培育了大量的高素质的专业人才。实验教学在专业课的教学中占有重要地位,是培养学生的探索能力和提高学生的探索意识的主要切入点,爱因斯坦曾说过：＂提出一个问题往往比解决一个问题更重要＂,然而在传统教育观念的思维模式的影响下,我国在以往教学中对实验教学的重视明显不足,对学生实验能力的培养还远远不够,与发达国家相比尚存在一定的差距,因此我们需要加大对实验教学的重视,改进实验教学的方案,为21世纪培养更多建设性的人才。     

Eye Movement Modeling Examples guide viewer eye movements but do not improve learning

Recent research has tested whether Eye Movement Modeling Examples (EMMEs) can effectively cue attention and improve learning. However, the effects of EMMEs are variable, and the degree to which viewers follow these cues remains unclear. In the current paper, we compared screen-captured instructional videos that included an EMME in the form of a transparent circular overlay depicting the instructor's gaze location with identical videos that lacked this cue. We observed that EMMEs drove viewer saccades to cued locations and resulted in shorter distances between viewer gaze and the EMME, but learning performance and video preference were unaffected by the presence of an EMME. We argue that EMMEs can effectively guide attention, but the range of circumstances under which they improve learning may be limited.     

BERT-SMAP: Paying attention to Essential Terms in passage ranking beyond BERT

Passage ranking has attracted considerable attention due to its importance in information retrieval (IR) and question answering (QA). Prior works have shown that pre-trained language models (e.g. BERT) can improve ranking performance. However, these simple BERT-based methods tend to focus on passage terms that exactly match the question, which makes them easily fooled by the overlapping but irrelevant (distracting) passages. To solve this problem, we propose a self-matching attention-pooling mechanism (SMAP) to highlight the Essential Terms in the question-passage pairs. Further, we propose a hybrid passage ranking architecture, called BERT-SMAP, which combines SMAP with BERT to more effectively identify distracting passages and downplay their influence. BERT-SMAP uses the representations obtained through SMAP to enhance BERT’s classification mechanism as an interaction-focused neural ranker, and as the inputs of a matching function. Experimental results on three evaluation datasets show that our model outperforms the previous best BERT${}_{base}$-based approaches, and is comparable to the state-of-the-art method that utilizes a much stronger pre-trained language model.