M in CoMputational thinking: How long does it take to read a book?

Even at the primary level, computational thinking (CT) can support young students to prepare for participating in futures that are immersed in data. In mathematics classrooms, there are few explanations of the ways CT can support students in formulating and solving complex problems. This paper presents an example of a primary classroom investigation (8-9 year olds) over seven lessons of the problem “How long does it take to read a book?” The aim is to illustrate ways a statistical investigation can provide context for CT and demonstrate how the two complement each other to solve problems involving mathematics. The findings highlight opportunities and challenges that students face across the elements of CT—decomposition, abstraction, pattern recognition and modelling, and generalization and algorithmic thinking, including recommendations for teaching.     

以生为本是开启后进生心灵之门的金钥匙

由于学校比较重视学生的智力发展而忽略了素质教育,导致后进生逐年增多,严重制约着教育教学质量的提高。因此,是否能有效地转化后进生是保证和提高教育教学质量的关键,是学校德育工作的重中之重,这也已成为广大教育工作者的共识。     

提高数学纠错实效的思考

初中学生随着学习科目的增多,知识的难度和综合性逐步加深,学习中存在一定的“夹生”或“认知遗误”等现象,作业中的错误随之越来越多。教师和学生都应把作业中的错误作为宝贵的资源,探求数学纠错的策略和方法,提高纠错的实效性。     

应用构造思想培养解题能力

主要通过构造数学模型,培养学生思维能力,通过构造辅助问题,培养学生建构能力以及通过想象,培养学生的思维开放性.运用构造思想,构造出一个新的数学形式,从而能使问题在此形式下获得解决.     

基于问题解决的《计算机应用基础》课的教学思考

结合作者多年的《计算机应用基础》课的教学经验,探讨"研究性学习 上机操作"的教学模式,通过解决实际问题,调动起学生学习的积极性、能动性、创造性,提高了学习效率,对学生综合能力的培养有很大好处。     

中学数学解题思维错误的成因及对策

正确处理学生的解题错误,不仅能提高学生解题能力,同时也从某种程度上反映教师的数学素养.这里从错误的特性,生成因素,以及对应策略三方面,并结合实际题例,进行简单的定性分析.     

布朗运动实验中常见的问题与解决的方法

布朗运动实验中经常出现微粒取材不易、凹坑制作不便、浊液容易干涸、光源易受影响等问题，这些问题困扰着教师。按照观察实验设计的基本理念——取材容易、结构简单、制作方便、重复性强、现象观察效果较好，从理论和实践上较详细地阐述了切实可行的解决问题的方法。     

Embellishing Problem-Solving Examples with Deep Structure Information Facilitates Transfer

Appreciation of problem structure is critical to successful learning. Two experiments investigated effective ways of communicating problem structure in a computer-based learning environment and tested whether verbal instruction is necessary to specify solution steps, when deep structure is already embellished by instructional examples. Participants learned to solve algebra-like problems and then solved transfer problems that required adjustment of learned procedures. Experiment 1 demonstrated that verbal instruction helped learning by reducing learners' floundering, but its positive effect disappeared in the transfer. More importantly, students transferred better when they studied with examples that emphasized problem structure rather than solution procedure. Experiment 2 showed that verbal instruction was not necessarily more effective than nonverbal scaffolding to convey problem structure. Final understanding was determined by transparency of problem structure regardless of presence of verbal instruction. However, verbal instruction had a positive impact on learners by having them persist through the task, and optimal instructional choices were likely to differ depending on populations of learners.     

Effects of performance feedback valence on perceptions of invested mental effort

We investigated whether the valence of performance feedback provided after a task, would affect participants’ perceptions of how much mental effort they invested in that same task. In three experiments, we presented participants with problem-solving tasks and manipulated the presence and valence of feedback between conditions (no, positive, or negative feedback valence), prior to asking them to rate how much mental effort they invested in solving that problem. Across the three experiments–with different problem-solving tasks and participant populations–we found that subjective ratings of effort investment were significantly higher after negative than after positive feedback; ratings given without feedback fell in between. These findings show that feedback valence alters perceived effort investment (possibly via task perceptions or affect), which can be problematic when effort is measured as an indicator of cognitive load. Therefore, it seems advisable to measure mental effort directly after each task, before giving feedback on performance.     

Demystifying computational thinking

This paper examines the growing field of computational thinking (CT) in education. A review of the relevant literature shows a diversity in definitions, interventions, assessments, and models. After synthesizing various approaches used to develop the construct in K-16 settings, we have created the following working definition of CT: The conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts. This definition highlights that CT is primarily a way of thinking and acting, which can be exhibited through the use particular skills, which then can become the basis for performance-based assessments of CT skills. Based on the literature, we categorized CT into six main facets: decomposition, abstraction, algorithm design, debugging, iteration, and generalization. This paper shows examples of CT definitions, interventions, assessments, and models across a variety of disciplines, with a call for more extensive research in this area.