Student Attitudes toward Learning Analytics in Higher Education: "The Fitbit Version of the Learning World"

Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students' knowledge, attitudes, and concerns about big data and learning analytics through four focus groups (N = 41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, “Uninformed and Uncertain,” represents students' lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; “Help or Hindrance to Learning,” “More than a Number,” and “Impeding Independence”; represented students' perceptions of the likely impact of learning analytics on their learning. “Driving Inequality” and “Where Will it Stop?” represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how “personal” vs. “collective” purposes or principles can intersect with “uniform” vs. “autonomous” activity. The findings highlight the need the need to engage students in the decision making process about learning analytics

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Course management support Application-iMaster. Report

Mestrado de dupla diplomação com a Université Libre de TunisNowadays databases are becoming bigger and bigger and the number of data records increases after the strong growth of investments in information and communication technologies, this is the case of the IPB. That is the reason why this platform is created. Our main goal was to simplify interpretation data through out academies and schools. The project’s goal is to design and implement Dashboard called Imaster.reporting which made him able to visualize informations about the master and allowed the director to generate reports. For the realization of this module we used the Frameworks ReactJS and .net Core. The solution also allows the user to make reports instantly with multiple extension PDF and CSV.Hoje em dia, as bases de dados estão se tornando cada vez maiores e o número de registros de dados aumenta após o forte crescimento dos investimentos nas tecnologias de informação e comunicação. É por isso que se criou esta plataforma. O principal objetivo era simplificar a interpretação de dados em nossas academias e escolas. O objetivo do projeto é criar e implementar um painel chamado Imaster.reporting que o tornou capaz de visualizar informações sobre o curso de mestrado e permitiu ao diretor gerar relatórios. Para a realização deste módulo utilizamos os Frameworks ReactJS e .net Core. A solução também permite que o utilizador faça relatórios instantaneamente com múltiplas extensões PDF e CSV

Connecting the Dots: Data Use in Afterschool Systems

Afterschool programs are seen as a way to keep low-income children safe and to foster the skills needed to succeed in school and life. Many cities are creating afterschool systems to ensure that such programs are high-quality and widely available. One way to do so is to ensure afterschool systems develop and maintain a data system.This interim report presents early findings from a study of how afterschool systems build their capacity to understand and improve their practices through their data systems. It examines afterschool data systems in nine cities that are part of The Wallace Foundation's Next Generation Afterschool System-Building initiative, a multi-year effort to strengthen systems that support access to and participation in high-quality afterschool programs for low-income youth. The cities are Baltimore, Md., Denver, Colo., Fort Worth, Texas, Grand Rapids, Mich., Jacksonville, Fla.,Louisville, Ky., Nashville, Tenn., Philadelphia, Pa., and Saint Paul, Minn.To date, research on data use in afterschool systems has focused more on the implementation of technology than on what it takes to develop and sustain effective data use. This study found that the factors that either enabled or hampered the use of data in afterschool systems—such as norms and routines, partner relationships, leadership and coordination, and technical knowledge—had as much to do with the people and process components of the systems as with the technology.Strategies that appear to contribute to success include:    Starting small. A number of cities intentionally started with a limited set of goals for data collection and use, and/or a limited set of providers piloting a new data system, with plans to scale up gradually.    Ongoing training. Stakeholders learned that high staff turnover required ongoing introductory trainings to help new hires use management information systems and data. Providing coaching and developing manuals also helped to mitigate the effects of turnover and to further the development of more experienced and engaged staff.    Outside help. Systems varied in how they used the expertise of outside research partners. Some cities identified a research partner who participated in all phases of the development of their data systems. Others used the relationship primarily to help analyze and report data collected by providers. Still others did not engage external research partner, but identified internal staff to support the system. In any of these scenarios, dedicated staffers with skills in data analytics were key.

From data to knowledge: Tableau dashboards as boundary objects in the knowledge ecology of a university

Information dashboards are increasingly important tools for organisations, helping them exploit data as an asset and make informed decisions. Existing visualisation design research stemming from the cognitive and perception sciences has tended to focus on the cognitive augmenting benefits of information visualizations for the individual in trying to accomplish a task, and make recommendations for design based on perceptual and cognitive principles. However, understanding the use to which information visualisations (in this case dashboards) are put in the management and operations of a large hierarchical bureaucracy that typify the modern organisation responding to complex and dynamic environments, is important for gaining insights that will guide their design, adoption and adaption in these organisations. An ethnographic inspired study was performed at a University who were in the process of adopting Tableau as a management reporting tool, during a period in which there were significant changes to HE environment. The study reports on the evolution of the dashboards, as mediating artefacts, in which the social process of designing takes place. Significantly, allowing communities of knowing to be intimately involved in the building of their own dashboards (through the concept of self-service) allows the dashboards to support the social sense-making roles of “perspective making and perspective taking”. The extent to which the dashboards are able to achieve this is the extent to which they are deemed useful in transforming data into effective actionable knowledge

Students’ experiences of learning analytics in academic advising for supporting self-regulated learning

Abstract. This qualitative thesis was located at the intersection between learning analytics and self-regulated learning where academic advising worked as a context. The examination was limited to self-regulation of behavior and further to three resource management strategies: time management, effort regulation and help seeking. Also, the examination of learning analytics was limited to visualizations developed in a research project called AnalyticsAI. Even though the importance of involving students’ perspectives to the development processes of learning analytics applications is well acknowledged, there are currently only few studies regarding it. The main goal of this thesis was to contribute by addressing this gap in previous research by providing insights how self-regulated learning can be supported via learning analytics according to students themselves. More precisely I was interested in finding answers to three research questions regarding students’ own challenges and needs for support concerning resource management strategies and progress in studies, students’ experiences of the visualizations under development and students’ expectations for their further development. Participants were ten students from the University of Oulu who attended the pilot study conducted in AnalyticsAI in the academic year 2019–2020. The data of this thesis was collected through semi-structured interviews with stimulated recall method, and was analyzed with qualitative theory directed content analysis in which transcriptions of interviews worked as research material. The results indicated that students in this study were well-achieving and reported only minor challenges and needs for support which generally had not affected their progress in studies. Students also had different preferences regarding the current visualizations and their use in advising context which appeared as mixed experiences. Generally students experienced that visualizations make needs for support more visible and therefore they were perceived to be especially useful for students with more challenges. Students also expected different, and sometimes even controversial, features from learning analytics. Therefore, giving students control over the choice of functionalities in learning analytics would be reasonable to consider in order to develop customizable and individually meaningful learning analytics. Also, in order to support self-regulated learning, it should be made sure that learning analytics provides feedback from all phases of self-regulated learning, since students experienced that the visualizations failed to provide support for planning future studies.Opiskelijoiden kokemuksia oppimisanalytiikan käytöstä akateemisessa ohjauksessa itsesäätöisen oppimisen tukemiseksi. Tiivistelmä. Tämä laadullinen pro gradu -tutkielma sijoittui oppimisanalytiikan ja itsesäätöisen oppimisen leikkauspisteeseen, jossa akateeminen ohjaus toimi kontekstina. Itsesäätöisen oppimisen tarkastelu rajautui käyttäytymisen säätelyyn ja tarkemmin kolmeen resurssienhallintastrategiaan: ajanhallintaan, ponnistelujen säätelyyn ja avun hakemiseen. Oppimisanalytiikan teemasta tarkastelu rajautui AnalytiikkaÄly-hankkeessa kehitettyihin omaopettajaohjauksessa käytettäviin visualisointeihin. Vaikka opiskelijoiden mukaan ottaminen oppimisanalytiikan sovellusten kehittämisprosesseihin on tiedostettu olevan tärkeää, tällä hetkellä on olemassa vain muutamia tutkimuksia aiheeseen liittyen. Tämän työn päätavoitteena on paikata tätä aiempien tutkimusten puutetta tarjoamalla syvempää ymmärrystä siitä, miten itsesäätöistä oppimista voidaan tukea oppimisanalytiikan avulla opiskelijoiden itsensä mukaan. Tarkemmin olin kiinnostunut löytämään vastauksia kolmeen tutkimuskysymykseen liittyen opiskelijoiden omiin haasteisiin ja tuentarpeisiin resurssienhallintastrategioista ja opintojen etenemisestä, heidän kokemuksiinsa kehitteillä olevista visualisoinneista sekä opiskelijoiden toiveista ja odotuksista visualisointien jatkokehittämiselle. Tutkittavat koostuivat kymmenestä Oulun yliopiston opiskelijasta, jotka osallistuivat AnalytiikkaÄly-hankkeen pilottitutkimukseen lukuvuonna 2019–2020. Aineistonkeruu tapahtui puolistrukturoitujen haastattelujen kautta hyödyntäen stimulated recall-metodia ja aineisto analysoitiin laadullisella teoriaohjaavalla sisällönanalyysilla, jossa haastattelujen litteroinnit toimivat tutkimusmateriaalina. Tutkimustulokset osoittivat tähän opinnäytetyöhön valikoituneiden tutkittavien olevan hyvin pärjääviä ja yleisesti omaavan vain vähäisiä haasteita ja tuentarpeita. Opiskelijoilla oli myös erilaisia mieltymyksiä kehitteillä olevista visualisoinneista ja niiden käytöstä ohjauksessa, mikä näyttäytyi eriävinä kokemuksina. Yleisesti opiskelijat kokivat kuvaajien onnistuvan visualisoimaan haasteita ja tuen tarpeita, jonka takia ne koettiin hyödyllisiksi erityisesti enemmän haasteita omaaville opiskelijoille. Opiskelijat myös odottivat oppimisanalytiikalta erilaisia ja toisinaan jopa vastakkaisia ominaisuuksia. Tästä syystä kontrollin antaminen opiskelijoille voisi olla mielekästä, jotta voitaisiin kehittää yksilöllisesti muokattavissa olevia ja siten merkitykselliseksi koettavia oppimisanalytiikan sovelluksia. Jotta itsesäätöistä oppimista voidaan tukea tehokkaasti, tulisi myös keskittyä tarjoamaan palautetta kaikkiin sen vaiheisiin liittyen, sillä tällä hetkellä opiskelijoiden kokemusten mukaan tulevien opintojen suunnittelu ei tule tarpeeksi tuetuksi

Perspectives of IR Professionals Regarding the Impact of Data Analytic Systems on Institutional Decision- Making.

The capacity for data analytical decision-making is not always optimal in institutions of higher education (Hawkins & Bailey, 2020). Data analytic decision making for this study is defined as any decision utilized to improve the process or outcome for any function of higher educational administration (Nguyen et al., 2020) including but not limited to: state appropriated funding (e.g. Campbell, 2018) improving graduation rates (e.g Moscoso-Zea, Saa & Luján-Mora, 2019), teacher instruction (e.g. Cai & Zhu, 2015), or student success (e.g. Foster & Francis, 2020). Many IR professionals still face obstacles pertaining to their ability to both utilize data analytical software as well as share data analytical findings across their respective clientele units outside of institutional research to impact institutional decision-making (Lehman, 2017). The literature is lacking concerning how IR professionals experience and navigate these critical aspects of data analytical decision-making support in higher educational institutions. The purpose of this study was to address the gap in the research by assessing the perspectives of IR professionals regarding their ability to utilize data analytic systems (e.g., analyzing, interpreting, sharing of data) to impact and strengthen institutional decision-making. The purpose of this study was also to understand how institutional culture (e.g., policies, operational processes, relevancy, conduciveness) influences the ability of IR professionals to utilize data analytic systems when sharing data findings or collaborating across their respective institutions to enhance institutional decision-making. Recommendations based on the study findings included stronger data governance for dashboards and data visualizations, expanding predictive analytics to enhance student success, and data literacy training with both utilizing data analytics software and interpreting data findings according to the context of individual institutions

Operationalizing successful strategic planning processes in a high performing community college

2019 Fall.Includes bibliographical references.This study assessed the effectiveness of the strategic planning processes in a high performing community college. Lake Area Technical Institute (LATI) in Watertown, South Dakota was identified as a high performing institution based on earning the 2017 Aspen Prize for Community College Excellence. The study utilized a qualitative, single site, case study to provide insight into the elements that led to a successful strategic planning process. The data collection included interviews with twenty-three employees including faculty, staff, and administration. A document analysis of relevant planning documents was conducted, as well as an observation of the college's strategic planning session, and observation of the institution's strategic planning and Aspen Prize related artifacts. The results of this study provide strategic planners insight into how a high performing institution created and successfully implemented a strategic plan. Four a priori codes, or main themes, were developed prior to the study to guide my research. These four themes included: employee perceptions of strategic planning, employee participation in the strategic planning process, implementing the strategic plan, and linking budgets and resources to support the strategic plan. Within these themes, findings suggest that the following factors contributed to successful planning efforts at LATI. Theme One indicated that an inclusive planning process that values employee engagement and a positive culture throughout the institution generated extensive support for the planning process. Theme Two indicated support for a cross-represented group of employees and external stakeholders in the process and most importantly, valuing the input received from those participants. The results from Theme Three indicated several steps that led to successful implementation: conducting an annual planning process, assigning responsibility to the initiatives that comprise the plan, utilizing committees or teams implement the initiatives, communicating the details of the strategic plan to the campus community through multiple methods, and regularly assessing the plan. Theme Four discusses the ongoing resource allocation process that occurs throughout the fiscal year that supports the strategic plan. The research also explored the impact of winning the Aspen Prize for Community College Excellence on the institution and how the Aspen process impacted the strategic planning process