Data team conversations: a sensemaking framework of teachers’ collaborative use of student data

Although national legislation and policy call for the use of student assessment data to support instruction, evidence suggests that teachers lack the knowledge and skills required to effectively use data. Previous studies have demonstrated the potential of training for increasing immediate teacher outcomes (i.e., knowledge, skills, and beliefs), yet research is still needed that investigates whether these immediate learning outcomes correspond to improved practices in reading and math instruction. Using the Early Childhood Longitudinal Survey: Kindergarten (2011), the present study sought to investigate whether data-focused training predicted teacher use of data for four prevalent decision-making outcomes: monitor progress on specific skills, identify skill deficits, monitor overall progress of students performing below benchmark, and determine placement in instructional tiers. Results indicate that professional development to use data to identify struggling learners and coursework focused on the use of assessment to select interventions and supports significantly predicted teachers’ frequent use of data across key decision-making dimensions in reading instruction. Results for math instruction differ in that more frequent data use was not consistent across outcomes, more training sessions were needed, and professional development to use data to guide instruction significantly predicted use of data to monitor students who performed below benchmark.

School Reform: The District Policy Implications of the Effective Schools Literature

In 2011 a ‘new’ model for school improvement was rolled out across 23 schools in the Billabong Network in regional Victoria. This chapter considers a network-led model of school improvement, its development, adjustment and implementation across the Billabong Network. Key elements of this model included: school improvement plans negotiated between school principals and the network, the planning for change implementing successive short cycle initiatives, all based on the use of student data to drive change processes. Capacity building was understood to be central to the success of the processes, and took a number of forms including identifying the skill/needs within a school relative to their school improvement goals and providing opportunities for ongoing professional learning to key staff so as to address these needs. The role of the regional network leader and the effects of mandated use of a template in the planning process is considered.

The problem of predicting early dropout of students of Russian universities is urgent and therefore requires the development of new innovative approaches to solve it. To solve this problem, it is possible to develop predictive systems based on the use of student data, available in the information systems of universities. This paper investigates machine learning models for predicting early student dropout trained on the basis of student characteristics and performance data. The main scientific novelty of the work lies in the use of explainable AI methods to interpret and explain the performance of the trained machine learning models. The Explainable AI methods allow us to understand which of the input features (student characteristics) have the greatest influence on the results of the machine learning models. (student characteristics) have the greatest influence on the prediction results of trained models, and can also help to understand why the models make certain decisions. The findings expand the understanding of the influence of various factors on early dropout of students.

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It involves interactive exploration of data using visualizations and automated data analysis to gain insight, and to ultimately make better decisions. It aims to support the sensemaking process in which information is collected, organized and analyzed to form new knowledge and inform further action. Interactive visual exploration of the data can lead to many discoveries in terms of relations, patterns, outliers and so on. It is difficult for the human working memory to keep track of all findings during a visual analysis. Also, synthesis of many different findings and relations between those findings increase the information overload and thereby hinders the sensemaking process further. The central theme of this dissertation is How to support users in their sensemaking process during interactive exploration of data? To support the sensemaking process in visual analytics, we mainly focus on how to support users to capture, reuse, review, share, and present the key aspects of interest concerning the analysis process and the findings during interactive exploration of data. For this, we have developed generic models and tools that enable users to capture findings with provenance, and construct arguments; and to review, revise and share their visual analysis. First, we present a sensemaking framework for visual analytics that contains three linked views: a data view, a navigation view and a knowledge view for supporting the sense-making process. The data view offers interactive data visualization tools. The navigation view automatically captures the interaction history using a semantically rich action model and provides an overview of the analysis structure. The knowledge view is a basic graphics editor that helps users to record findings with provenance and to organize findings into claims using diagramming techniques. Users can exploit automatically captured interaction history and manually recorded findings to review and revise their visual analysis. Thus, the analysis process can be archived and shared with others for collaborative visual analysis. Secondly, we enable analysts to capture data selections as semantic zones during an analysis, and to reuse these zones on different subsets of data. We present a Select & Slice table that helps analysts to capture, manipulate, and reuse these zones more explicitly during exploratory data analysis. Users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data slices. Finally, exploration overviews and searching techniques based on keywords, content similarity, and context helped analysts to develop awareness over the key aspects of the exploration concerning the analysis process and findings. On one hand, they can proactively search analysis processes and findings for reviewing purposes. On the other hand, they can use the system to discover implicit connections between findings and the current line of inquiry, and recommend these related findings during an interactive data exploration. We implemented the models and tools described in this dissertation in Aruvi and HARVEST. Using Aruvi and HARVEST, we studied the implications of these models on a user’s sensemaking process. We adopted the short-term and long-term case studies approach to study support offered by these tools for the sensemaking process. The observations of the case studies were used to evaluate the models.

Sufficient knowledge is available about the definition, details and differences of qualitative content and thematic analysis as two approaches of qualitative descriptive research. However, identifying the main features of theme as the data analysis product and the method of its development remain unclear. The purpose of this study was to describe the meaning of theme and offer a method on theme construction that can be used by qualitative content analysis and thematic analysis researchers in line with the underpinning specific approach to data analysis. This methodological paper comprises an analytical overview of qualitative descriptive research products and the meaning of theme. Also, our practical experiences of qualitative analysis supported by relevant published literature informed the generation of a stage like model of theme construction for qualitative content analysis and thematic analysis. This paper comprises: (i) analytical importance of theme, (ii) meaning of theme, (iii) meaning of category, (iv) theme and category in terms of level of content, and (v) theme development. This paper offers a conceptual clarification and a pragmatic step by step method of theme development that has the capacity of assisting nurse researchers understand how theme is developed. As nursing is a pragmatic discipline, nurse researchers have tried to develop practical findings and devise some way to “do something” with findings to enhance the action and impact of nursing. The application of a precise method of theme development for qualitative descriptive data analysis suggested in this paper helps yield meaningful, credible and practical results for nursing.

PurposeAlthough E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of stakeholders' satisfaction. This research focuses on an intelligence-driven solution to recommend the most effective approach to education policymakers by considering the unique characteristics of all components within the educational system (course type, student and teacher characteristics, and technological features) to achieve a sustainable E-learning system.Design/methodology/approachThrough a systematic literature review and qualitative content analysis, a conceptual model of the critical components influencing E-learning quality and satisfaction has been developed. The proposed model comprises six main dimensions: usage, service quality, learning system quality, content quality, perceived usefulness, and individual characteristics. These dimensions are further divided into 15 components and 114 sub-components. A data mining process encompassing two scenarios has been designed to prioritize the components impacting E-learning success.FindingsIn the first scenario, data mining techniques identified the most influential components based on the features outlined in the conceptual model. According to the results, the components affecting E-learning quality enhancement in the studied case are “usage purpose, system loyalty, technical and supportive system quality, and student characteristics.” The second scenario examines the impact of individuals' personality types and learning styles on E-learning satisfaction across various aspects (Average System Satisfaction, Overall System Satisfaction, Efficiency and Effectiveness, Skill Enhancement, and Personal Enhancement). The findings reveal that, with an accuracy of over 70%, E-learning satisfaction for diplomat and guard learners is influenced by the alignment between “course learning style” and “student-suggested course learning style.” Conversely, for analyzer and researcher types, satisfaction levels are impacted by the “learning style compatible with their personality type.”Originality/valueImplementing a dynamic model founded on data mining enables educational institutions to personalize the E-learning experience for each individual as much as possible. The study’s findings indicate that “achieving higher satisfaction levels in the E-learning process is not necessarily contingent upon providing a learning style congruent with learners' personality types.” Rather, perceived and suggested learning styles exert a more profound influence. Consequently, providing prescriptive principles for higher education institutions seeking to enhance E-learning quality is inadvisable. Instead, adopting a dynamic, knowledge-based process that furnishes recommendations to policymakers for each course—tailored to the specific course type, teaching records, current processes and technology, and student type—is highly recommended.

Early warning dashboards in higher education analyze student data to enable early identification of underperforming students, allowing timely interventions by faculty and staff. To understand perceptions regarding the ethics and impact of such learning analytics applications, we conducted a multi-stakeholder analysis of an early-warning dashboard deployed at the University of Michigan through semi-structured interviews with the system's developers, academic advisors (the primary users), and students. We identify multiple tensions among and within the stakeholder groups, especially with regard to awareness, understanding, access and use of the system. Furthermore, ambiguity in data provenance and data quality result in differing levels of reliance and concerns about the system among academic advisors and students. While students see the system's benefits, they argue for more involvement, control, and informed consent regarding the use of student data. We discuss our findings' implications for the ethical design and deployment of learning analytics applications in higher education. Early warning dashboards in higher education analyze student data to enable early identification of underperforming students, allowing timely interventions by faculty and staff. To understand perceptions regarding the ethics and impact of such learning analytics applications, we conducted a multi-stakeholder analysis of an early-warning dashboard deployed at the University of Michigan through semi-structured interviews with the system's developers, academic advisors (the primary users), and students. We identify multiple tensions among and within the stakeholder groups, especially with regard to awareness, understanding, access, and use of the system. Furthermore, ambiguity in data provenance and data quality result in differing levels of reliance and concerns about the system among academic advisors and students. While students see the system's benefits, they argue for more involvement, control, and informed consent regarding the use of student data. We discuss our findings' implications for the ethical design and deployment of learning analytics applications in higher education.

The analysis of narrative data frequently raises methodological concerns oscillating between, on the one hand, the necessity to structure and highly heterogeneous and descriptive data for operationalizing analysis and, on the other hand, the interest to preserve the subtlety and context of narration for enhancing analysis. Based on existing research that evidences the inherent coherence of qualitative analysis for narrative data and the operational advantage of quantifying narrative data via textual analysis, this paper presents a methodological exploration combining textual analysis and qualitative content analysis to analyze narrative data. Positioning this methodological process within an abductive approach, this research assesses an analysis conducted on 75 narrative texts written by employees and managers about their work and shows that 1) the textual analysis, with its quantifying techniques, helps peruse and structure narrative data, particularly heterogeneous and massive data; 2) the interaction between quantifying techniques supported by textual analysis and qualitative content analysis based on interpretation favors the interaction between deductive reasoning and inductive reasoning during the analytical process and therefore reinforces the interpretative robustness of qualitative content analysis; 3) if qualitative content analysis cannot intrinsically dissociate from the subjective interpretation, textual analysis might provide relative objectifying adjustments to the subjectivity of interpretation. The paper ends by presenting propositions and recommendations with regard to the utilization of quantifying techniques in the qualitative analysis of narrative data.

a theoretical perspective. Next, we describe the current state of knowledge about congruence between the two curricula. Finally, we present a study that describes reading instruction congruence and provides some hypotheses about the causes and effects of such congruence. There are currently many different philosophies concerning reading instruction. Because different basal reading materials are developed from diverse instructional ideologies, they often vary in the rate of introduction of new words, the nature of the words introduced (determined by frequency of use, grapho-phonic regularity, or presumed interest), the emphasis on silent or oral reading, relationship of pictures to text (in terms of information value), relationship of text structure to natural language patterns, predictability of text, and order of introduction of skills. The instruction early readers receive (e.g., decoding based vs. meaning based) is reflected in their reading performance, particularly for less able readers. The reading errors of children whose early instruction emphasized decoding tend to match the letter-sound relationships of the actual words but are semantically inappropriate-sometimes even nonsense words. Children taught with a meaning-based approach tend to produce errors that are semantically appropriate but bear little relationship to the printed words (e.g., Barr The Elementary School Journal Volume 85, Number 4 ? 1985 by The University of Chicago. All rights reserved. 0013-5984/85/8504-0001$01.00

The integration of different analytical approaches allows harnessing their respective benefits. Nonetheless, the integration of varied methods is challenging, primarily due to the scarcity of comprehensive guidelines for such complex analyses. This paper introduces a five-step approach for combining two distinct research methods: qualitative content analysis and narrative analysis as a useful tool for researchers working under critical realist paradigm, or those who just wish to use both – categorizing and connecting approaches – in their research analysis. Qualitative content analysis plays a crucial role in categorizing insights from data. However, an exclusive reliance on content analysis might result in the loss of important contextual aspects associated with these insights. Consequently, narrative analysis becomes valuable, as it enables linking diverse elements in the data, such as the subject of study, its context, associated events, and identified categories. The process of combining the qualitative content analysis and narrative analysis method introduced in the current study was formulated during a doctoral research project within a critical realist paradigm, which necessitated a thorough consideration of both the subject matter and its context. In response to the absence of guidelines for combining content and narrative analysis, the author developed and tested a unique process during her research project. Employing the suggested approach of connecting content and narrative analysis can assist researchers, particularly those applying the critical realist paradigm in the process of generating contextually situated yet generalisable results. From a practical standpoint, innovative research methods and more comprehensive insights from academic studies enhance our understanding of various patterns in the business and management landscape.

Purpose of the article: To review recent research into the connection between brand archetypes and masculinity archetypes in advertising and assess them from a consumer engagement perspective. The study focused primarily on two main questions. The first was to find out which brand archetypes and masculinity archetypes are the most common in advertisements concentrating on traditional and modern masculinity. The second main question was to find out which brand and masculinity archetypes get more approval from the consumers and which have more positive feedback.Methodology/methods: The researchers used qualitative content analysis, video content analysis, and sentiment analysis. The qualitative content analysis was conducted using the Nvivo 11 qualitative data analysis software to help organise, analyse, and find relevant insights in the text. The authors chose to have a mixed content analysis of conventional and direct content analysis. The qualitative content and sentiment analysis were used to analyse consumer opinions from 2400 YouTube comments on certain advertisements where masculinity is identified as a critical concept.Scientific aim: To see whether the brand archetype theory and masculinity archetype theory are compatible in analysing consumer opinions about masculinity advertisements.Findings: The results from the video content analysis show that the most common brand archetypes in masculinity advertisements are the Caregiver, Ruler, Lover, and Hero. Regarding masculinity archetypes, the most common ones are the King, Lover, and Warrior.Conclusions: The most positive consumer discourse was for the advertisements with the Lover, Creator, Everyman, Explorer, and Hero brand archetypes. Concerning masculinity archetypes, the most positive consumer discourse was with the Lover and Warrior masculinity archetypes.

Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process.

Background: A considerable body of literature attests to the efficacy of client and therapist collaborative goal setting to achieving optimal rehabilitation outcomes. Collaborative goal setting and shared decision making relies on good communication, thus potentially disadvantaging people with aphasia. Aims: This study aims to identify the similarities and differences between client goals and therapist goals in rehabilitation for people with aphasia and to explore reasons why any differences occur. Methods & Procedures: Three speech-language pathologists and four people with aphasia participated in in-depth semi-structured interviews to identify rehabilitation goals. All the interviews were transcribed and analysed using qualitative content analysis. Outcomes & Results: Results indicated both matching and mismatching of goals between the clients and the speech-language pathologists. Matched goals tended to focus on communication outcomes. Mismatched goals were those associated with the client's desire to return to previously valued activities. Reasons for the mismatching included: impaired communication made collaboration on goal setting difficult, the service-delivery approach, the goal was perceived to be outside the speech-language pathologist's scope of practice, and the goal was not considered to be appropriate within the confines of the rehabilitative situation. Conclusions: This study highlights the need for speech-language pathologists to understand their clients' goals and how these can be incorporated into rehabilitation. A re-examination of some professional beliefs was highlighted. Future research may lead to educational resources that enable better collaborative goal setting between therapist and client so that outcomes of rehabilitation are optimised.

In a constantly changing world, schools need to adapt. Difficulties, successes, and experienced challenges can be important driving forces for school improvement. This study aims to develop a comprehensive understanding of the situation and decision-making processes related to school improvement strategies in socially disadvantaged areas, where schools face particularly challenging circumstances. The study comprises data from 100 Austrian schools, that is, answers to open-ended questions in a survey. A mixed-methods approach was applied. The qualitative data were analyzed using qualitative content analysis. The data were processed, frequencies of themes were determined, and latent profile analysis was applied. The results show the diversity of challenges, difficulties, strengths, and development perspectives reported by schools. The results of the latent profile analysis indicate that there are different underlying profiles in the orientation of development perspectives towards strengths, difficulties, challenges, and/or not identified factors.