A bibliometric perspective of learning analytics research landscape

ABSTRACT

Learning analytics is an emerging field of research, motivated by the wide spectrum of the available educational information that can be analysed to provide a data-driven decision about various learning problems. This study intends to examine the research landscape of learning analytics to deliver a comprehensive understanding of the research activities in this multidisciplinary field, using scientific literature from the Scopus database. An array of state-of-the-art bibliometric indices is deployed on 2811 procured publication datasets: publication counts, citation counts, co-authorship patterns, citation networks and term co-occurrence. The results indicate that the field of learning analytics appears to have been instantiated around 2011; thus, before this time period no significant research activity can be observed. The temporal evolution indicates that the terms ‘students’, ‘teachers’, ‘higher education institutions’ and ‘learning process’ appear to be the major components of the field. More recent trends in the field are the tools that tap into Big Data analytics and data mining techniques for more rational data-driven decision-making services. A future direction research depicts a need to integrate learning analytics research with multidisciplinary smart education and smart library services. The vision towards smart city research requires a meta-level of smart learning analytics value integration and policy-making.     

Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics

A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux–velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction.We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the ‘assess’ stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, ‘forecast’, involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step ‘develop options’ for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different “what if” scenarios.     

Human–computer interaction in evolutionary visual software analytics

Software evolution is made up of changes carried out during software maintenance. Such accumulation of changes produces substantial modifications in software projects and therefore vast amounts of relevant facts that are useful for the understanding and comprehension of the software project for making additional changes. In this scenario, evolutionary visual software analytics is aimed to support software maintenance, with the active participation of users, through the understanding and comprehension of software evolution by means of visual analytics and human computer interaction. It is a complex process that takes into account the mining of evolutionary data, the subsequent analysis of the mining process results for producing evolution facts, the use of visualizations supported by interaction techniques and the active participation of users. Hence, this paper explains the evolutionary visual software analytics process, describes a framework proposal and validates such proposal through the definition and implementation of an architecture.     

Mapping the study of learning analytics in higher education

ABSTRACT

In recent years, the application of technological innovation in higher education has become more and more widely spread, and technological innovation has been improving the level of education. In the research of higher education with innovation technology, one of the main focuses is on the dynamic data which can lay a foundation for the analysis of educational activities by learning analytics. The dynamic data created by technological innovation will become the key basis for analytical research and development in higher education. The methods and analysis results of learning analytics will directly affect decision-making and strategy about higher education. In this paper, we use bibliometric and visualisation methods to review the literature, in order to highlight the development of learning analytics in higher education. Using bibliometric analysis, our study depicts the development process of the main methods used in learning analytics, and summarises the current situation in this field, which increases the level of understanding provided by those studies. Finally, we summarise the research hotspots and study trends, which will be useful for future study in this field.     

面向出租车出行规律的预测式可视分析方法

针对传统交通数据可视分析方法缺乏预测分析能力的问题,提出了基于出租车出行数据的预测式可视分析方法,支持用户更有效地探索未来的交通状况.在可视分析模型中,提出了结合天气、星期几等多种非交通因素的预测模型,提高了预测的准确度;提出了基于预测数据和广义地点类型约束的路径规划方法,获得了更优的路径规划结果;以多种可视化手段分析和预测了出租车司机的运营状况,帮助司机进行运营决策.以温州市出租车数据进行的实验结果表明,与传统方法相比,文中方法能更准确地预测交通状况和运营状况,并获得更合理的路径规划结果.     

Analytics Capabilities and the Decision to Invest in Analytics

The increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm’s decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.     

Analyzing Relationships in Terrorism Big Data Using Hadoop and Statistics

We used big data software Hadoop in Google News to collect complex high-velocity, high-volume terrorism information. We used big text search to code the factors of interest into nominal fields. We integrated new fields and records into an existing database drawn from other researchers. Our testable hypothesis was that there was a significant relationship between terrorist group ideology and terrorist attack type. Then we used correspondence analysis in SPSS to test our hypothesis. Our hypothesis was supported, so we developed a symmetric model to visualize the hidden relationships between terrorist ideology and attack type. Our purpose was to demonstrate how statistical software methods may be applied in big data analytics. These methods will generalize to other researchers and practitioners. The finding of a significant relationship between terrorist ideology and attack type may generalize to supply chain operations and national security planning.     

Hinted Star Coordinates for Mixed Data

Mixed data sets containing numerical and categorical attributes are nowadays ubiquitous. Converting them to one attribute type may lead to a loss of information. We present an approach for handling numerical and categorical attributes in a holistic view. For data sets with many attributes, dimensionality reduction (DR) methods can help to generate visual representations involving all attributes. While automatic DR for mixed data sets is possible using weighted combinations, the impact of each attribute on the resulting projection is difficult to measure. Interactive support allows the user to understand the impact of data dimensions in the formation of patterns. Star Coordinates is a well-known interactive linear DR technique for multi-dimensional numerical data sets. We propose to extend Star Coordinates and its initial configuration schemes to mixed data sets. In conjunction with analysing numerical attributes, our extension allows for exploring the impact of categorical dimensions and individual categories on the structure of the entire data set. The main challenge when interacting with Star Coordinates is typically to find a good configuration of the attribute axes. We propose a guided mixed data analysis based on maximizing projection quality measures by the use of recommended transformations, named hints, in order to find a proper configuration of the attribute axes.     

Improving Cyber Security Awareness and Training Programs with Data Analytics

ABSTRACT

This article it is a modest attempt to explore the issues facing cyber security awareness and training programs and potential benefits of using learning analytics, an emerging field in data analytics, for combining existing data sources to provide additional value to these programs. This article was written under the assumption that awareness and training are valid preventive controls and therefore the pros and cons of implementing such programs are not being discussed.     

Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data

The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time) on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye‐tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time‐independent and time‐aware models, achieving median F1 scores of about 0.7–0.8 on leave‐one‐session‐out k‐fold cross‐validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers.     

A Framework for Interactive t-SNE Clustering

In this paper, we describe our progress in creating the framework for an interactive application that allows humans to actively participate in a t-SNE clustering process. t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique that maps high dimensional data sets to lower dimensions that can then be visualized for human interpretation. By prompting users to monitor outlying points during the t-SNE clustering process, we hypothesize that users may be able to make clustering faster and more accurate than purely algorithmic methods. Further research would test these hypotheses directly. We would also attempt to decrease the lag time between the various components of our application and develop an intuitive approach for humans to aid in clustering unlabeled data. Research into human assisted clustering can combine the strengths of both humans and computer programs to improve the results of data analysis.     

Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education.The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance learning.     

The impact of on-demand metacognitive help on effortful behaviour: A longitudinal study using task-related visual analytics

This longitudinal study investigates the differences in learners' effortful behaviour over time due to receiving metacognitive help—in the form of on-demand task-related visual analytics. Specifically, learners' interactions (N = 67) with the tasks were tracked during four self-assessment activities, conducted at four discrete points in time, over a period of 8 weeks. The considered and coded time points were: (a) prior to providing the metacognitive help; (b) while the task-related visual analytics were available (treatment); (c) after the removal of the treatment; and (d) while the option to receive metacognitive help was available again. To measure learners' effortful behaviour across the self-assessment activities, this study utilized learners' response-times to correctly/wrongly complete the tasks and on-task effort expenditure. The panel data analysis shown that the usage of metacognitive help caused statistically significant changes in learners' effortful behaviour, mostly in the third and fourth phase. Statistically significant changes were detected also in the usage of metacognitive help. These results provide empirical evidence on the benefits of task-related visual analytics to support learners' on-task engagement, and suggest relevant cues on how metacognitive help could be designed and prompted by focusing on the “task”, instead of the “self”.     

面向微博主题的可视分析研究

随着微博的发展,其影响力日益增大,对微博主题内容进行分析具有重要的价值.主题模型技术能够从文本数据中提取主题,但是,由于微博文本短、随意性大、信息量小等特点,微博主题的分析具有一定的难度.提出了一个微博主题可视分析系统,利用多种互相关联的视图与丰富的交互手段,支持用户对主题模型结果进行分析与探索.系统结合了微博数据的特点,引入微博用户与时间因素,支持分析者从多角度对微博主题进行全面分析.系统支持用户在主题可视分析的基础上,通过交互操作对主题进行编辑,从而改进主题模型,提高模型的准确性和可靠性.案例分析结果表明,提出的系统可以有效地帮助用户分析微博主题和修正主题.     

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.     

模具CAD／CAM柔性系统中零件与结构三维信息建模方法研究

本文讨论了用三维实体结构型技术（ＣＳＧ）发开模具ＣＡＤ／ＣＡＭ系统，并且运用面向对象的分析方法分别建立了零件三维信息及其相互关系的数据结构，由此可以作为开展模具ＣＡＤ／ＣＡＭ系统软件的基础。     

Business Analytics-Based Enterprise Information Systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.     

Exploration of Player Behaviours from Broadcast Badminton Videos

Understanding an opposing player's behaviours and weaknesses is often the key to winning a badminton game. This study presents a system to extract game data from broadcast badminton videos, and visualize the extracted data to help coaches and players develop effective tactics. Specifically, we apply state-of-the-art machine learning methods to partition a broadcast video into segments, in which each video segment shows a badminton rally. Next, we detect players' feet in each video frame and transform the player positions into the court coordinate system. Finally, we detect hit frames in each rally, in which the shuttle moves towards the opposite directions. By visualizing the extracted data, our system conveys when and where players hit the shuttle in historical games. Since players tend to smash or drop shuttles under a specific location, we provide users with interactive tools to filter data and focus on the distributions conditioned by player positions. This strategy also reduces visual clutter. Besides, our system plots the shuttle hitting distributions side-by-side, enabling visual comparison and analysis of player behaviours under different conditions. The results and the use cases demonstrate the feasibility of our system.     

Episodes and Topics in Multivariate Temporal Data

The term ‘episode’ refers to a time interval in the development of a dynamic process or behaviour of an entity. Episode-based data consist of a set of episodes that are described using time series of multiple attribute values. Our research problem involves analysing episode-based data in order to understand the distribution of multi-attribute dynamic characteristics across a set of episodes. To solve this problem, we applied an existing theoretical model and developed a general approach that involves incrementally increasing data abstraction. We instantiated this general approach in an analysis procedure in which the value variation of each attribute within an episode is represented by a combination of symbols treated as a ‘word’. The variation of multiple attributes is thus represented by a combination of ‘words’ treated as a ‘text’. In this way, the the set of episodes is transformed to a collection of text documents. Topic modelling techniques applied to this collection find groups of related (i.e. repeatedly co-occurring) ‘words’, which are called ‘topics’. Given that the ‘words’ encode variation patterns of individual attributes, the ‘topics’ represent patterns of joint variation of multiple attributes. In the following steps, analysts interpret the topics and examine their distribution across all episodes using interactive visualizations. We test the effectiveness of the procedure by applying it to two types of episode-based data with distinct properties and introduce a range of generic and data type-specific visualization techniques that can support the interpretation and exploration of topic distribution.