教师效能与校长评价:揭示教学信心的关键因素

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本文档《教师效能与教师能力评级》发表于1985年7月的《心理学在学校》(Psychology in the Schoolr)第22卷,由Landa Trentham、Steven Silvern和Richard Brogdon三位作者代表 Auburn University 进行了一项研究。研究主题聚焦在教师的职业信念(教师效能)与校长对其教学能力的评价之间的关系,同时探讨了影响这两者的一些关键因素,包括教师的背景变量和人口统计特征。 研究样本涵盖了东南部一个州的15个学区,共155位教师参与。研究采用交叉验证的方法,通过多元回归分析发现,几个变量与教师效能得分显著相关。具体来说,校长的教师能力评级、教师的出生顺序以及他们是否愿意再次选择教育作为职业是这些关联中的重要因素。这表明,教师的职业信念与其职业满意度和选择有着紧密的联系。 进一步,通过区分性分析(discriminant analysis),研究发现可以基于四个显著变量将教师分为高、中等和低能力水平。其中,教师效能得分被证明是区分教师能力的一个关键指标,能够将80.52%的教师归入正确的等级,并解释了群体间29%的变异。这揭示了在评估教师能力时,校长的评价不仅在单个学校内有较高的一致性,而且能在不同学区之间体现某种程度的一致性。 然而,论文也提到了对美国教育体系的批评,暗示虽然教师效能与能力评级之间存在联系,但教育系统的整体评价体系可能需要更深入地考虑教师的专业发展、工作环境和政策支持等因素,以提升整个教育质量。这项研究不仅提供了实证依据来理解教师效能与能力评级的关系,也为教育政策制定者提供了有价值的数据支持,以优化教师的专业发展路径和提高教学质量。

这一段讲的是什么:Abstract—A recent trojan attack on deep neural network (DNN) models is one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker’s chosen trojan trigger. Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation. This work builds STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system and focuses on vision system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of predicted classes for perturbed inputs from a given deployed model—malicious or benign. A low entropy in predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input—a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on three popular and contrasting datasets: MNIST, CIFAR10 and GTSRB. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers. Using CIFAR10 and GTSRB, we have empirically achieved result of 0% for both FRR and FAR. We have also evaluated STRIP robustness against a number of trojan attack variants and adaptive attacks. Index Terms—Trojan attack, Backdoor attack

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