Third-Grade Students Investigate and Represent Data

Results of a collaborative project between two mathematics education faculty, a third-grade teacher, and her beginning third-grade students are presented. The purpose of this project was to collect information from these students as they thought through how to ask, collect, and then answer their own data-driven questions. These young students exhibited a growing understanding, sophistication, and comfort with the use and application of data and data terminology as they developed their strategies for collecting, analyzing, and presenting their results.     

Helping Business Students Acquire the Skills Needed for a Career in Analytics: A Comprehensive Industry Assessment of Entry‐Level Requirements

Fact‐based decision making is changing job functions within organizations more than any other technology. Analytics, once the purview of the data scientist, is now spread throughout organizations. No longer is there a single job title, job function, or set of required skills and credentials for an analytics career. Companies have moved away from seeking applicants with a specific degree to now recruiting analytics talent based on required skill sets. For more than a decade, business schools have been developing new programs in analytics in response to industry's needs. However, in developing meaningful career‐ready professionals, business programs must understand the skills required across different analytics job functions. In this article, the authors present a comprehensive assessment of the skills sought by employers when considering a candidate for an entry‐level analytics position. The authors describe the demand for various types of analytics professionals, identify the job titles and functions with the most significant demand, and then draw a comparison of the job requirements of hard skills, soft skills, software skills, and credentials between three of the most sought‐after analytics areas: data science, data analytics, and business analytics. The authors conclude by providing faculty and administrators with recommendations on how to adapt their courses and programs to provide students with the fundamental preparation necessary for careers in data science, data analytics, and business analytics.     

Applying Learning Analytics to the Scholarship of Teaching and Learning

In this article we report on the findings of a project funded by the Australian Office for Learning and Teaching and entitled “Learning Analytics: Assisting Universities with Student Retention.” While this project was primarily focused on retention as a potential outcome of learning analytics, its application could be related to the broader concept of student success. Student success allows for a focus on pedagogy and the use of learning analytics for the improvement of learning and teaching with a firm scholarly evidence base. The data gathered for the project provide the background for a discussion about the potential of learning analytics to inform the practice of the Scholarship of Teaching and Learning. A case study demonstrates the potential of this approach. Overall, clear pedagogical questions are important in the application of learning analytics to the Scholarship of Teaching and Learning, and we suggest potential ways to explore pedagogical questions with big data methods.     

Learning to Learn From Benchmark Assessment Data: How Teachers Analyze Results

Although interim assessments are currently promoted as a mechanism for improving teaching and student learning, we know little about how teachers use this data to modify instruction. This article presents findings from a larger study on teachers’ use of interim assessment information in elementary mathematics. We address the following questions: (a) How do the Philadelphia teachers in our sample analyze benchmark assessment results, (b) how do they plan instruction based on these results, and (c) what are their reported instructional responses to such results? To answer these questions, we interviewed all 3rd- and 5th-grade teachers in five average- and above-average-performing elementary schools three times during the 2006–07 school year. We found that although the teachers in our study used interim assessment results to gain information about students’ learning in mathematics, teachers did not use interim assessments to make sense of students’ conceptual understanding. Furthermore, teachers’ tendency to interpret student errors as procedural missteps was paralleled by a trend toward procedural instructional responses.     

Performance Analytics: The Missing Big Data Link Between Learning Analytics and Business Analytics

Big data analytics technology is changing the human capital development landscape. Global benchmarking trends reveal a gap between what executives expect from our profession and what we are currently capable of delivering. Emerging big data performance analytics technology offers our profession the capability to answer this growing executive expectation to diagnose the current and projected strengths and vulnerabilities of their workforce. It provides real‐time evidence that our performance improvement solutions optimize human and organizational performance.     

学习分析技术:挖掘大数据时代下教育数据的价值

当前,大数据时代已经来临,教育领域同样积累了海量数据。教育领域已经部署了众多的学习管理系统,在这些软件系统中存储着海量的学习者信息及学习过程数据。如何利用这些数据,使这些数据转变为信息、知识,并为教学决策、学习优化服务,已成为教育工作者以及学习者们所关注的内容。学习分析技术有助于发挥学习过程数据的价值,使数据成为审慎决策、过程优化的重要依据。该文介绍了国内外学习分析技术研究现状,归纳出学习分析技术的关键技术及分析模式,并以实例从不同用户视角包括管理者、辅导教师、学习者展示了学习分析技术在网络学习过程分析中的应用过程。     

Understanding privacy and data protection issues in learning analytics using a systematic review

The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.

Practitioner notes

What is already known about this topic

• Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
• Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.

What this study contributes

• Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
• Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
• Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.

Implications for practice and/or policy

• Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
• Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
• Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.

     

学习分析：助推大数据时代高校教师在线专业发展

随着“大数据时代”的到来,一股席卷全球的智能化在线教育浪潮正在蔓延,高校传统的教学模式趋向颠覆,高校教师的职业发展也将受到重大挑战。众多大学生在在线学习时将会产生海量的数据,高校教师如何挖掘、分析这些数据,对改进自身教学实践、促进自身专业发展都具有丰富的价值。学习分析在大数据时代的高等教育中被广泛应用已成必然趋势,并具有非常广阔的应用前景,高校教师应该具备较强的数据分析能力。学习分析从以下四个方面促进大数据时代高校教师在线专业发展：可以提升作为在线学习者的高校教师的学习效率,激发其自主的专业发展意识;可以提高高校教师作为在线教学者的教学效率,发展其在线教学实践智慧;可以提高高校教师作为研究者的研究绩效,提升其对学生在线学习的服务能力;可以提高高校教师作为管理者的管理效率,提升其在线教学领导力。     

Reading literacy in Compulsory Secondary Education: an exploration of the difficulties in the use of information to answer questions / La competencia lectora en la Educación Secundaria Obligatoria: descripción y dificultades del uso de información para responder preguntas

Abstract

This paper is a review of studies about processes and secondary school students’ difficulties with these processes when they use information to answer questions. The PISA program uses this kind of task-oriented reading situation to evaluate reading literacy, which is also a common reading situation in educational settings. Four key strategies are identified to successfully perform this kind of task: decide how to read the information, comprehend the task, decide when to search for information in order to solve the task, and self-regulate that search process. Moreover, this paper provides accurate information about the students’ difficulties in becoming skilled readers when they use information to answer questions. This information might help teachers and educational counselors in the design of interventions to improve reading literacy in schools.     

Students as Producers of Interactive Data Visualizations—Digitally Skilled to Make Their Voices Heard

The study explores students’ production of interactive visualized stories with visual analytics (VA). The aim is to understand emerging interactions in classrooms of grades 7–9 students when visual storytelling methods are playing a part in producing social science content. The dual aspects of visual literacy, information retrievement paired with the creation of interactive visualized stories, are crucial. Video captures of students working in groups and of what happens on their screens are conducted. The results show that students can handle the technical aspects of a VA application, but interpretation of visualized statistics is challenging. The study suggests that VA has potential to strengthen students’ ability to handle huge amounts of data and increase the possibilities for young people to take part in society.(Keywords: digital technology, digital skills, democracy, civic knowledge, social science education, visual analytics, visual storytelling, visual literacy, digital literacy)     

Making learning whole: an instructional approach for mediating the practices of authentic science inquiries

This design experiment aimed to answer the question of how to mediate the practices of authentic science inquiries in primary education. An instructional approach based on activity theory was designed and carried out with multi-age students in a small village school. An open-ended learning task was offered to the older students. Their task was to design and implement instruction about the Ice Age to their younger fellows. The objective was collaborative learning among students, the teacher, and outside domain experts. Mobile phones and GPS technologies were applied as the main technological mediators in the learning process. Technology provided an opportunity to expand the learning environment outside the classroom, including the natural environment. Empirically, the goal was to answer the following questions: What kind of learning project emerged? How did the students’ knowledge develop? What kinds of science learning processes, activities, and practices were represented? Multiple and parallel data were collected to achieve this aim. The data analysis revealed that the learning project both challenged the students to develop explanations for the phenomena and generated high quality conceptual and physical models in question. During the learning project, the roles of the community members were shaped, mixed, and integrated. The teacher also repeatedly evaluated and adjusted her behavior. The confidence of the learners in their abilities raised the quality of their learning outcomes. The findings showed that this instructional approach can not only mediate the kind of authentic practices that scientists apply but also make learning more holistic than it has been. Thus, it can be concluded that nature of the task, the tool-integrated collaborative inquiries in the natural environment, and the multiage setting can make learning whole.     

Written justifications to multiple-choice concept questions during active learning in class

Increasingly, instructors of large, introductory STEM courses are having students actively engage during class by answering multiple-choice concept questions individually and in groups. This study investigates the use of a technology-based tool that allows students to answer such questions during class. The tool also allows the instructor to prompt students to provide written responses to justify the selection of the multiple-choice answer that they have chosen. We hypothesize that prompting students to explain and elaborate on their answer choices leads to greater focus and use of normative scientific reasoning processes, and will allow them to answer questions correctly more often. The study contains two parts. First, a crossover quasi-experimental design is employed to determine the influence of asking students to individually provide written explanations (treatment condition) of their answer choices to 39 concept questions as compared to students who do not. Second, we analyze a subset of the questions to see whether students identify the salient concepts and use appropriate reasoning in their explanations. Results show that soliciting written explanations can have a significant influence on answer choice and, when it does, that influence is usually positive. However, students are not always able to articulate the correct reason for their answer.     

浅谈历史课堂上学生提出的“意外问题”

在中学历史课堂教学中,学生有时会对教师的讲述或教科书的叙述提出一些意外性的问题,使教师不知道如何解答。根据问题的特点,可以将其大致分为两种类型:史事类的问题和认识类的问题。学生在课堂上提出意外问题的原因,一是同学生的认知习惯及思维特点有关,二是同中学历史知识的特点有关。学生提出意外问题这一现象,有着一定的教学上的意义,为此,教师应不断调整教学策略,更好地与学生共同完成教学的任务。     

Teaching critical questions about argumentation through the revising process: effects of strategy instruction on college students’ argumentative essays

The effects of self-regulated strategy development revising instruction for college students that targeted the use of argumentation schemes and critical questions were assessed in three conditions. In the first condition, students were taught to revise their essays by asking and answering critical questions about the argument from consequences and argument from example schemes while writing about controversial topics. In the second condition, students were taught to revise their essays by using argumentation schemes to justify their standpoint, but did not learn the critical questions. In the third condition, students received no instruction about either the argumentation schemes or the critical questions. Compared to students in the contrasting conditions, those who were taught to ask and answer critical questions wrote essays that were of higher quality, and included more counterarguments, alternative standpoints, and rebuttals. These findings indicate that strategy instruction that includes critical standards for argumentation increases college students’ sensitivity to alternative perspectives.     

学习分析:正在浮现中的数据技术

随着教育信息化的普及与逐渐深入,学习管理系统已经获取并存储了大量的有关学生复杂学习行为的数据,从这些数据中挖掘出改进教学系统、提升学习效果的信息,在教育信息化领域一直有着巨大的吸引力。因此,有必要从分析数据以改进学习的角度,对日益受到关注的学习分析技术进行解读。首先,学习分析技术是测量、收集、分析和报告有关学生的学习行为以及学习环境的数据,用以理解和优化学习及其产生的环境的技术。接着,综述学习分析技术的发展,指出其在教育中有着广泛的应用前景和巨大的发展潜力:学习分析技术可作为教师教学决策、优化教学的有效支持工具,也可为学生的自我导向学习、学习危机预警和自我评估提供有效数据支持,还可为教育研究者的个性化学习设计和增进研究效益提供数据参考。最后,提出学习分析技术也存在隐私、准确性和兼容性等诸多挑战和问题。     

Visualization of Twitter Data in the Classroom

The expression “big data” is ubiquitous in the business world today, but few undergraduate business students have the opportunity to gain practical experience with how new business analytics tools can be used in decision making. This article describes a set of hands‐on labs that prepare students to incorporate streaming data analysis into group research projects. Splunk is used to help students analyze and visualize streaming social media data. An evaluation of student projects and student survey results show that this practical approach of training students to manipulate and visualize big data was largely successful in achieving instructional goals.     

Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course

In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large‐scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post‐course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content.     

“How do You Know this Answer?” – Children’s Use of Text Data and General Knowledge in Story Comprehension

This study investigates how, and to what extent, young readers (7–8 year-olds) use text information or their prior knowledge when answering comprehension questions about narrative texts. The children were asked to explain how they found out their responses by answering the following question: “how do you know this answer?” Their answers and justifications were analysed both qualitatively and quantitatively. The text proved to be the main source of information for these readers. However, sometimes the children seemed to ignore the text and over rely on their prior knowledge to answer the questions. The procedure of asking children to justify their answers was shown to be a good way of specifying more precisely some of their problems in text comprehension. It also seemed to encourage them to look back at the text and review their responses and, as such, it could be considered a useful tool to improve children’s reading comprehension.     

Gender Differences in the Use and Effectiveness of Personal Response Devices

The use of personal response devices (or “clickers”) in the classroom has increased in recent years. While few quantitative studies on the effectiveness of clickers have been published, it is generally reported that clickers have been well-received by the students who use them. Two separate populations (Winter 2006 and Spring 2006) of engineering students were given clickers to use during a general chemistry class. Clicker use was compared to student grades for each course. During both terms, a higher percentage of female students than male students “actively participated” in the lectures, where active participation was defined as answering more than 75% of the clicker questions over the course of the term. Active male students earned final grades about 10 points higher than non-active male students. Active female students, however, scored only about 5 points higher than non-active female students. Student learning was assessed by comparing performance on exam questions and clicker questions with similar content. Students who answered clicker questions correctly were 11–13% more likely to answer the corresponding exam questions correctly than were students who did not answer the clicker question. In this paper, we demonstrate the effectiveness of clicker use in the classroom and examine gender differences associated with this use.     

Examining the Relationship Between Comparative and Self-Focused Academic Data Visualizations in At-Risk College Students' Academic Motivation

This qualitative study focuses on capturing students' understanding two visualizations often utilized by learning analytics-based educational technologies: bar graphs, and line graphs. It is framed by Achievement Goal Theory—a prominent theory of students' academic motivation—and utilizes interviews (n = 60) to investigate how students at risk of college failure interpret visualizations of their potential academic achievement. Findings suggest that visualizations only containing information about students themselves (i.e., self-focused affordances) evoked statements centered on mastering material. Visualizations containing information about students and a class average (i.e., comparative information), on the other hand, evoked responses that disheartened students and/or made them feel accountable to do better. Findings from this study suggest the following guidelines for designing visualizations for learning analytics-based educational technologies: (1) Never assume that more information is better; (2) anticipate and mitigate against potential misinterpretations—or harmful alternative interpretations—of visualizations; and (3) always suggest a way for students to improve. These guidelines help mitigate against potential unintended consequences to motivation introduced by visualizations used in learning analytics-based educational technologies. (Keywords: motivation, visualizations, learning analytics, Achievement Goal Theory, college students, educational technologies)