中职学校学生学业成绩评价体系在计算机课程中的应用

"教育评价是杆旗,指向哪里打哪里。"学生学业成绩评价体系在中职学校教育中占据着举足轻重的地位,该文论述了学生学业成绩评价体系改革的必要性和重要性以及新的学生学业成绩评价体系在计算机应用课程中的应用,从而调动学生学习的积极性,激发学生参与学习过程,促使教师更新教学观念。同时对学生学业成绩评价体系的完善和发展提出一些建设性意见。     

中职学生信息技术课学业成绩的评价探究

教学评价是教学过程的一个重要环节,对学生能否做出正确、客观、科学的评价将在很大程度上影响学生的学习态度和学习兴趣.中等职业学校学生在校除学习高中阶段必要的文化课基础知识,同时还要学习专业知识,掌握专业技能本领.本文通过分析中职学生学业成绩评价的多元化方法,结合信息技术学科的特点,针对目前存在于信息技术学科里面的学业成绩评价弊端,提出相关对策.     

运用多媒体提高高中物理习题课复习效率

高中物理习题课教学,是一种重要的基本课堂教学模式,运用好多媒体在习题课中作用,可以使学生在课堂上轻松、活泼、主动地学习,从而较大限度地提高课堂教学效益,提高学生学习兴趣和学业成绩.     

校园生活规律性与学业成绩的相关性研究

高校学生的日常行为生活中,影响学生学业成绩的因素是教育管理者关注的重要问题之一。当前高校信息化建设为教育领域与大数据技术的融合提供了数据支撑。基于某高校学生的用电信息,通过真实熵度量学生校园日常生活中的规律性,并通过计算真实熵与学业成绩的相关系数,说明校园生活的规律性与学业成绩是相关联的。教育管理者可以以此作为决策依据,可制定提高学生生活规律性的措施,从而提高学生的学业成绩;还可以根据学生生活和学习的规律性,及时找出潜在的学业成绩不良者,提前加以指导或干预,做到教育的针对性和个性化。     

多维度混合式学习绩效评价模式的研究

高职学生普遍存在缺乏学习兴趣、学习效率低下等低绩效现象。在以在线学习与课堂面授相结合的混合式教学模式下,有必要研究如何有效利用学习绩效评价提升学生学习兴趣,提高学习效率。本文从线上学习、线下学习和学业成绩三个维度出发,构建混合式学习绩效评价模式,既有过程性的评价,又有结果性的评价,实践证明有利于从内部激励和提高学生的学习积极性,提高教学质量。     

基于综合评价模型的大学生学业成绩测量与预测

定量地了解学生的日常行为、身心健康和学业成绩之间的关系是迈向个性化教育的重要一步。与先前基于问卷调查的研究相比,笔者收集了某高校理学院800名本科生的校园一卡通和综合素质测评表的数据,选取12个影响因素进行主成分分析,提取了学习勤奋度、饮食规律性和身心健康3个特征并与学业成绩建立非线性回归模型。同时,利用得到的非线性回归模型对学业成绩进行预测。基于这些分析,教育管理者可以在必要时实施针对性的干预以帮助学生提高学业成绩。     

以评促学，激发学生的潜能

在新课程实验中，学生学业成绩的测试与评价仍然是重中之重。以往，我们总是把更多的目光定位于学生的终结性评价，其实，在评价中，更应该关注的是学生的学习过程，确保学生通过学习过程的精细化获得最佳的学习效果。     

与"课标"同行-教师手记以评促学,激发学生的潜能

在新课程实验中,学生学业成绩的测试与评价仍然是重中之重.以往,我们总是把更多的目光定位于学生的终结性评价,其实,在评价中,更应该关注的是学生的学习过程,确保学生通过学习过程的精细化获得最佳的学习效果.     

对提高中职CAD教学中学生学习兴趣的研究

根据现阶段中职校的教学现状,针对中职学生的学生特点、学习特点的分析,提出了CAD教学中提高学生学习兴趣,直至学业成绩的策略.     

招生模式“多元化”背景下的高职院校计算机基础课程分层教学法的研究

当前高等职业院校主要有统考招生、单考招生、自主招生等多种招生模式,这使得学生个体间的知识基础差异很大。因此,在高职院校的计算机基础课程中采用分层教学法就显得尤为重要了。在计算机基础课程中进行教学目标、教学主体、教学内容、教学过程、教学评价的分层教学实践,为高职院校提供了一个提高学生的学习兴趣、学习自信心和学业成绩的实践平台。     

应用型高校翻转课堂教学模式可行性调查研究

本文从学生视角研究翻转课堂教学模式开展的可行性。采用问卷调查法,对应用型高校的学生进行随机抽样。分别从学生课余学习时间、希望得到的帮助、学习工具与平台、课堂态度及表现等四个维度进行调查研究。结果发现:首先,约有70%的学生有充足的课余学习时间,能够保证翻转课堂课前学习任务的完成,仍有30%的学生因课余时间不足或学习态度不端正,不能保证课前学习任务的完成;其次,学生自主解决问题的能力与学业水平成正相关性。成绩优秀的学生喜欢自主解决问题,成绩较差的学生喜欢依靠同伴的帮助解决。学生成绩处于良好和中等的学生,喜欢教师给予指导和帮助;再次,学生具备翻转课堂硬件设备。但对学业水平较差的学生以及低年级的学生,要进行MOOCS学习平台和教学管理平台的培训;最后,学生受传统教学模式影响深刻,仍喜欢课堂上以教师讲授为主。因此教师要充分分析其可行性,对存在的问题采取措施进行解决,才能保证翻转课堂顺利开展。     

Mobile technology: students perceived benefits of apps for learning neuroanatomy

Technology‐enhanced learning is expanding rapidly because of research showing the benefits for learners in terms of engagement, convenience, attainment and enjoyment. Mobile learning approaches are also gaining in popularity, particularly during practical classes and clinical settings. However, there are few systematic studies evaluating the impact of tablet devices on students’ learning in practical settings. The main aim of this three‐year study was to gather rigorous evidence about students’ use of apps on a preconfigured tablet device in a neuroanatomy practical class, their perceptions of this and the impact of the intervention on learning outcomes, using data collected from three cohorts of students between 2011 and 2013. Results showed that students made extensive use of resources provided, considered the devices to be beneficial for learning, and found them to be easy to use with minimal support and training. Students’ ownership of touch screen devices increased significantly during the trial period as did their use of devices for academic study. Analysis of examination scores showed a statistically significant increase in performance for neuroanatomy‐related questions after the introduction of tablet devices.     

基于阅读认知诊断的学生表现预测

面向学生的表现预测（试题得分）是在线教育领域重要的研究课题。但传统认知诊断的预测准确性较低,协同过滤方法难以保证预测结果的可解释性。此外,由于目前方法预测时大多利用了学生的试题作答记录,因而不能预测无作答记录的学生在试题上的表现。学生作答试题之前,通常会阅读一些辅助性文本类学习材料。阅读认知诊断即学生阅读学习材料的内容往往可以反映出学生的知识状态（知识点掌握情况）,从而有助于预测学生表现。为此,提出一种基于阅读认知诊断的知识状态建模方法。利用学生阅读学习材料的内容,量化其对学习材料的潜在认知程度。结合教育学假设,量化学习材料的难度。利用上述两个量化结果,根据项目反应理论,计算出学生对学习材料的实际掌握程度,据此建模学生的知识状态并预测其在试题上的表现。在实际数据集上进行实验,实验结果表明所提方法可以保证预测结果的准确性与可解释性,也可以预测出无作答记录的学生表现。     

Different experiences,different effects: a longitudinal study of learning a computer program in a network environment

Students’ previous computer experience has been widely considered an important factor affecting subsequent computer performance. However, little research has been done to examine the contributions of different types of computer experience to computer performance at different time points. The present study compared the effects of four types of computer experience on 30 graduate students’ learning of a statistical program over one semester. Among the four types of computer experience, students’ earlier experience of using computer network systems was found to affect their initial performance of learning the statistics program, but the experience of using statistical programs, the experience of email programs, and the length of using computers did not. These findings suggest complex relationships between students’ computer experience and their computer performance and have implications for both learning and teaching computer programs and understanding the transfer of learning.     

Instructing students on effective sequences of examples and problems: Does self-regulated learning improve from knowing what works and why?

Nowadays, students often practice problem-solving skills in online learning environments with the help of examples and problems. This requires them to self-regulate their learning. It is questionable how novices self-regulate their learning from examples and problems and whether they need support. The present study investigated the open questions (1) to what extent students' (novices) task selections align with instructional design principles and (2) whether informing them about these principles would improve their task selections, learning outcomes, and motivation. Higher education students (N = 150) learned a problem-solving procedure by fixed sequences of examples and problems (FS-condition), or by self-regulated learning (SRL). The SRL participants selected tasks from a database, varying in format, complexity, and cover story, either with (ISRL-condition) or without (SRL-condition) watching a video detailing the instructional design principles. Students' task-selection patterns in both SRL conditions largely corresponded to the principles, although tasks were built up in complexity more often in the ISRL-condition than in the SRL-condition. Moreover, there was still room for improvement in students' task selections after solving practice problems. The video instruction helped students to better apply certain principles, but did not enhance learning and motivation. Finally, there were no test performance or motivational differences among conditions. Although these findings might suggest it is relatively ‘safe’ to allow students to independently start learning new problems-solving tasks using examples and problems, caution is warranted: It is unclear whether these findings generalize to other student populations, as the students participating in this study have had some experience with similar tasks or learning with examples. Moreover, as there was still room for improvement in students' task selections, follow-up research should investigate how we can further improve self-regulated learning from examples and practice problems.     

Active learning through collaborative knowledge building using an automatic free-text scoring system in a b-learning environment

According to active learning, students should be responsible for their own learning. Automatic free-text scoring allows teachers to provide open-ended questions with their correct answers to a computer system, so when students answer the questions, they get immediate feedback (a score, a comment, or both). However, teachers are usually overloaded with many tasks, and they may not have time to create the questions with the correct answers. Therefore, in the 2012/2013 academic year, we asked a group of 124 Pre-Primary and Primary Education students to become the creators of the questions and their correct answers in groups in a free-text scoring system, so the questions use learners’ language, not teachers’ language. From them, 41 students (group of involved students, GIS) fulfilled all the requirements during the course. Our hypothesis was that GIS would be able to increase their academic performance and levels of engagement compared to the rest of the students. The results gathered provide statistic evidence to support that hypothesis. This study pretends to help teachers who want to increase the academic performance and levels of engagement of their students in courses that they may find boring and unrelated to the main topic of their degree, or not directly related with their main academic interests.     

An Investigation of a Joyful Peer Response System: High Ability vs. Low Ability

Existing peer response approaches majorly take place in traditional educational contexts, where students may not be highly motivated. On the other hand, game-based learning takes a joyful way to enhance students’ motivation. Accordingly, we propose a joyful peer response (JPR), where game-based learning and peer response are integrated together. Furthermore, two empirical studies were conducted to investigate how high- and low-ability students perform in the JPR and how their perceptions are associated with their performance. Regarding the former, the findings suggest that the JPR is beneficial to high- and low-ability students. Regarding the latter, the findings imply that the writing performance of the high-ability students is majorly related to their perceptions of game elements. On the other hand, the writing performance of the low-ability students is related to their perceptions of both game elements and peer response.     

Using a genetic algorithm to determine optimal complementary learning clusters for ESL in Taiwan

This paper proposes a strategy for using students’ complementary competencies in cooperative learning to increase their English learning performance. The concept of complementary learning is based on the idea that teaching is learning. The foundation of the complementary learning concept is composed of three stages proposed to derive the optimal learning clusters—input stage, genetic algorithm (GA) stage, and output stage. In tests and verification of the feasibility of using optimal complementary learning clusters in increasing students’ English learning outcome, comparisons between the experimental group (the optimal complementary learning clusters) and the control group showed that students in the experimental group had higher performances in listening, speaking, and reading competencies than those in the control group. Finally, according to the respective importance weights of different English competencies in different learning objectives, the fuzzy linguistic terms were applied to derive optimal complementary learning clusters to maximize students’ learning outcome.     

Data mining for adaptive learning sequence in English language instruction

The purpose of this paper is to propose an adaptive system analysis for optimizing learning sequences. The analysis employs a decision tree algorithm, based on students’ profiles, to discover the most adaptive learning sequences for a particular teaching content. The profiles were created on the basis of pretesting and posttesting, and from a set of five student characteristics: gender, personality type, cognitive style, learning style, and the students’ grades from the previous semester. This paper address the problem of adhering to a fixed learning sequence in the traditional method of teaching English, and recommend a rule for setting up an optimal learning sequence for facilitating students’ learning processes and for maximizing their learning outcome. By using the technique proposed in this paper, teachers will be able both to lower the cost of teaching and to achieve an optimally adaptive learning sequence for students. The results show that the power of the adaptive learning sequence lies in the way it takes into account students’ personal characteristics and performance; for this reason, it constitutes an important innovation in the field of Teaching English as a Second Language (TESL).     

Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms

Identifying students’ learning styles has several benefits such as making students aware of their strengths and weaknesses when it comes to learning and the possibility to personalize their learning environment to their learning styles. While there exist learning style questionnaires for identifying a student's learning style, such questionnaires have several disadvantages and therefore, research has been conducted on automatically identifying learning styles from students’ behavior in a learning environment. Current approaches to automatically identify learning styles have an average precision between 66% and 77%, which shows the need for improvements in order to use such automatic approaches reliably in learning environments. In this paper, four computational intelligence algorithms (artificial neural network, genetic algorithm, ant colony system and particle swarm optimization) have been investigated with respect to their potential to improve the precision of automatic learning style identification. Each algorithm was evaluated with data from 75 students. The artificial neural network shows the most promising results with an average precision of 80.7%, followed by particle swarm optimization with an average precision of 79.1%. Improving the precision of automatic learning style identification allows more students to benefit from more accurate information about their learning styles as well as more accurate personalization towards accommodating their learning styles in a learning environment. Furthermore, teachers can have a better understanding of their students and be able to provide more appropriate interventions.