Learning analytics and policy (LAP)

Despite the growing popularity of learning analytics in higher education, its development status in Asia was barely studied. This paper reports a study on the development of learning analytics in higher education in Asia. Semi-structured interviews were conducted with eight senior managers or senior academics from various tertiary institutions in Asia. The participants were asked about their institutions’ position on learning analytics, the progress in its implementation, factors leading to effective implementation, and challenges encountered, if any. The results showed that in those institutions where learning analytics has been implemented, it aimed mainly at enhancing student retention, pedagogy and student learning experience. Its effective implementation relies on support from senior management, and taking students’ views into account in decision-making. The participants’ institutions encountered difficulties due to teachers’ and students’ concerns, such as the increased workload and data privacy, as well as technical issues in data collection, processing and analysis. In short, though starting late in Asia, learning analytics has been gradually gaining attention and is being implemented. The future directions of research and practices in learning analytics are also discussed.

Learning Analytics is a dynamic interdisciplinary field that encompasses educational sciences and state-of-the-art technology, methods and systems from various fields of computing such as data science, data visualization, software engineering, human-computer interaction, statistics, artificial intelligence, with various stakeholders, e.g. instructors, students, department and college administrators, practitioners, university top managers, computer scientists, IT experts, and software developers. Despite some current achievements and initial developments in Learning Analytics, we are still in a very early stage of development of sophisticated technologies and well-thought practices, tools and applications in this field as well as understanding the impact of Learning Analytics on (a) student learning and privacy, and (b) faculty instruction and autonomy. This paper presents the up-to-date research findings and outcomes of a multi-aspect project on Smart Learning Analytics at Bradley University (USA). It describes the obtained research outcomes about student perception and attitude to Learning Analytics on an academic course level and corresponding Learning Analytics-based pedagogy.

This article introduces the special issue from SoLAR’s Learning Analytics and Knowledge conference. Learning analytics is an emerging field incorporating theory and practice from numerous disciplines to investigate how learner interactions in digital environments can provide actionable data about the learning process. As the field continues to expand there is a timely opportunity to evaluate its ongoing maturation. This evaluation could be in part informed by regular scientometric analyses from both the Journal and Conference publications. These analyses can collectively provide insight into the development of learning analytics more broadly and assist with the allocation of resources to under-represented areas for example.

Recent years have seen a growing recognition of the value of positioning teaching as a design science. In this context, we see a great potential in the possible synergy between educational design, learning analytics and teacher inquiry. This synergy is demonstrated in the form of C omputer S upported C urriculum A nalysis, D esign and E valuation for S cience E ducation in A frica ( CASCADE ‐ SEA ), a computer‐based support tool that assists teachers in creating exemplary lesson materials for secondary level science and mathematics in southern A frica. The purpose of the present study is to articulate how teachers might be supported in the synergistic processes that are integral to educational design. It does so by revisiting existing data collected during the design, development and evaluation of CASCADE ‐ SEA , and sharing the results of a retrospective analysis in light of recent developments in learning analytics, learning design and teacher inquiry. The retrospective analysis revealed insights related to each of the aforementioned themes. For learning analytics, those were social structures of learning and teaching practices; pedagogy‐driven collection and analysis of data; and the use of data to inform teaching and learning. For learning design, the findings pertained to the language, practices and tools used to achieve particular educational aims. For teacher inquiry, the findings relate to the empirical process of question formulation, methods selection, collection and analysis of data, and sharing/reflecting on the findings as well as overall process. Based on the retrospective analysis, design considerations are offered for a modern application to replace the practical and effective desktop software that was developed during the 1990s.

Higher education institutions are developing the capacity for learning analytics. However, the technical development of learning analytics has far exceeded the consideration of ethical issues around learning analytics. We examined higher education academics’ knowledge, attitudes, and concerns about the use of learning analytics though four focus groups (N = 35). Thematic analysis of the focus group transcripts identified five key themes. The first theme, ‘Facilitating learning’, represents academics’ perceptions that, while currently unrealized, there could be several benefits to learning analytics that would help their students. Three themes; ‘Where are the ethics?’, ‘What about the students!’, and ‘What about me!’ represented academics’ perceptions of how learning analytics could pose some considerable difficulties within a higher education context. A final theme ‘Let’s move forward together’ reflected that despite some challenges and concerns about learning analytics, academics perceived scope for learning analytics to be beneficial if there is collaboration between academics, students, and the university. The findings highlight the need to include academics in the development of learning analytics policies and procedures to promote the suitability and widespread adoption of learning analytics in the higher education sector.

The development and implementation of learning analytics as a mechanism to support student success has been an emerging trend within Higher Education. Previous research identifies that learning analytics is an innovative educational development but recognises that little attention has been paid to evaluating its effectiveness or pedagogic usefulness. Researchers recognise that learning analytics is a new field in need of further research to aid its credibility within the educational arena. This research study provides a better understanding of learning analytics to support student success through the examination of opportunities and challenges of learning analytics from a multi-stakeholder perspective. This study also demonstrates how learning analytics can be successfully implemented within Higher Education. Through an interpretivist paradigm, this cross-sectional research study captures the unique experiences of students, academic staff and learning analytics experts. Data collected through twelve semi-structured interviews and three student focus groups enabled the researcher to gather a broad understanding of learning analytics from those involved and provides an holistic portrayal from this cultural group. The main findings of this study suggest that learning analytics need to have a clear context and purpose within Higher Education to ensure successful development, effectiveness and pedagogic usefulness. Effective organisational change, culture, academic and student engagement, ownership and motivation are paramount. Findings also indicate disparities in the implementation of learning analytics within Higher Education, which require resolution to ensure success, and there is some discussion about how challenges can be overcome to ensure effective institutional adoption and student success.

Learning analytics is a significant area of technology-enhanced learning (TEL) that has emerged during the last decade. This review of the field begins with an examination of the technological, educational, and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives, and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining, and academic analytics. Finally, it examines developing areas of learning analytics research and identifies a series of future challenges.

With the arrival of 'big data; in education, the potential was recognised for learning analytics to track students' learning, to reveal patterns in their learning, or to identify at-risk students, in addition to guiding reform and supporting educators in improving teaching and learning processes [1]. Learning Analytics dashboards have been used at all levels, including institutional, regional and national level [2]. In classroom use, while learning visualisations are often based on counts of activity data or interaction patterns, there is increasing recognition that learning analytics relate to learning, and should therefore provide pedagogically useful information [3]. While increasing numbers of technology-enhanced learning applications are embracing the potential of learning analytics at the classroom level, often these are aimed at teachers. However, learners can also benefit from learning analytics data (e.g. [4][5]).

With the need in the global change research project for the land -use/land-cover change information, most international and regional research organization or groups have put amounts of efforts to improve of the dynamics monitoring and database updating techniques. With the pressure on nature environment from increasing population and decreasing farmland becoming significant more and more in China, the farmland urban dynamics in historical and current times, even the change trends in the future, should be monitored and analyzed serving for regional and national social, economic and environmental sustainable development in the long future. Based on spatial and temporal series of land-use/land-cover database resources, Chinese Academy of Sciences designed a sampling framework for monitoring farmland and urban area dynamics in regional and national level. In order to test the accuracy of the sampling schema for national and regional level, we took two provinces area into overall covered change detecting process with TM images data through being interpreted by digitalization on the screen. The result shows that our stratified random sampling schema is suitable for monitoring land -use/land-cover change at national and regional level with quick response, high accuracy and low expenses. The land-use/land-cover change (LUCC) information can update the LUTEA database for global change research during certain period so that the forecasting process and evaluating analysis on land resources and environment under human and natural driving force will get essential data and produce valuable conclusions.

Data-informed teacher inquiry into their own practices should help teachers pick most effective teaching methods for each particular group of students and teaching situation. However, there is not much research into the thought processes that teachers employ when making these decisions. This study is an attempt at finding out how data about teaching and learning from physical classrooms, collected with digital tools, could help teachers in adjusting their teaching methods and strategies to improve practice. The study explores how 12 Estonian teachers perceive their own technology use and teacher inquiry (TI) practices, and how much this corresponds to reality. Also, how data-analysis skills satisfy their actual needs and what problems they encounter within a technology-enhanced TI process. Survey data and TI diaries are analyzed using descriptive statistics and qualitative content analysis, and the teachers’ perceived knowledge of technology and teacher inquiry is compared to their actual application of these in 18 interventions. In addition, thematic analysis is applied to TI diary entries about teachers’ sense-making and interpretation of the collected data to provide insights into teachers’ decision-making processes, which would enable improvements to teacher inquiry models and further inform the development of different teaching and learning analytics (TLA) tools. Finally, teachers’ concerns connected with different stages in TI facilitation are calculated to identify how these relate to teachers’ application of TI.

Video analysis is a staple of the education research community. For many contemporary education researchers, participation in the video coding process serves as a rite of passage. However, recent developments in multimodal learning analytics may help to accelerate and enhance this process by providing researchers with a more nuanced glimpse into a set of learning experiences. As an example of how to use multimodal learning analytics towards these ends, this paper includes a preliminary analysis from 54 college students, who completed two engineering design tasks in pairs. Gesture, speech and electro-dermal activation data were collected as students completed these tasks. The gesture data was used to learn a set of canonical clusters (N=4). A decision tree was trained based on individual students' cluster frequencies, and pre-post learning gains. The nodes in the decision tree were then used to identify a subset of video segments that were human coded based on prior work in learning analytics and engineering design. The combination of machine learning and human inference helps elucidate the practices that seem to correlate with student learning. In particular, both engagement and disengagement seem to correlate with student learning, albeit in a somewhat nuanced fashion.

The article studies the influence of financial factors on the effective implementation of the conception of smart economy at different levels, reveals the influence of factors on the development of smart economy at different levels, presents a hierarchy of levels of smart economy implementation. It is defined that the key levels of smart economy implementation are national, regional, local and business levels. Accordingly, at each of these levels, the features and key aspects of financing smart economy programs are studied. It is further defined that at the national level, the key sources and areas of funding are the development of innovations, the formation of technology parks and innovation clusters, funds for which can be attracted from national, international sources and individual companies that are interested in the implementation of projects at a high level. The regional level is interested in the implementation of regional programs, funding for which, according to research, is provided from regional development funds, regional budget, bonds, national and international grants, co-financing. The study confirms that the role of finance in the development of regions or localities is quite significant, and programs to support smart cities or localities are developing quite actively. The local level is implemented through projects with an emphasis on digitalization, accordingly, these are attracted investments, grants and subsidies, public-private partnerships, city bonds, grants, and crowdfunding. At the business level, the implementation of smart projects requires attracting finance primarily through corporate investments, especially within the framework of corporate social responsibility programs, green investment development, venture financing, green or social bonds, etc. All these sources of funding are often combined with international grants and loans from organizations. The key levels of financing of the smart economy and the forms of its implementation have been identified, taking into account the limitations and opportunities for the development of national economic systems in the globalized world. This became possible due to the analysis of key prerequisites and examples of practical implementation of the smart economy at different levels, the peculiarities of the possibility of attracting financial resources for development at the national, regional, local and business levels.

Background:In 2015, the UN General Assembly set Sustainable Development Goals (SDGs), in which SDG 3.2 called for all countries to reach a neonatal mortality to at least as low as 12 per 1000 live births by 2030. As the SDG era enters its final years, especially after the COVID-19 pandemic, it is time to take stock of worldwide trends in terms of neonatal mortality and progress towards SDG target 3.2. We aimed to evaluate the time trends in neonatal mortality from 1990 to 2023 with projections towards SDG target 3.2 at the global, regional, and national levels.Materials and methods:Annual neonatal deaths and neonatal mortality rate (NMR) between 1990 and 2023 at the global, regional, and national levels were collected from a dataset complied by the UN Inter-agency group for Child Mortality Estimation. The percentage changes in neonatal deaths and the estimated annual percentage changes (EAPCs) in NMRs at the global, regional, and national levels were calculated. The year in which the location with an NMR above 12 deaths per 1000 live births would achieve the SDG 3.2 NMR target was estimated using time-series analyses.Results:Globally, the number of neonatal deaths decreased by 56.42% from 5.24 million in 1990 to 2.28 million in 2023 and the NMR decreased by an average of 2.54% (95% CI: 2.44%, 2.63%) per year from 36.70 deaths per 1000 live births in 1990 to 17.33 deaths per 1000 live births in 2023. The global NMR is expected to achieve SDG target 3.2 by 2036. At the regional level, the NMR decreased significantly in all SDG regions between 1990 and 2023 but remained above SDG target 3.2 in Sub-Saharan Africa, Central and Southern Asia, and Western Asia and Northern Africa, with NMRs of 26.41, 20.74, and 12.80 deaths per 1000 live births in 2023, respectively. Sub-Saharan Africa and Central and Southern Asia are projected to reach the SDG 3.2 target by 2068 and 2041, respectively. At the national level, the NMR increased significantly in Dominica (EAPC = 3.95; 95% CI: 3.74, 4.17), Botswana (EAPC = 1.92; 95% CI: 1.14, 2.70), Grenada (EAPC = 1.06; 95% CI: 0.48, 1.63), and Eswatini (EAPC = 0.32; 95% CI: 0.19, 0.44) between 1990 and 2023. Among the 200 countries and territories worldwide, 125 have already met SDG target 3.2, and 7 are expected to do so by 2030. Of the remaining 68 countries that would not achieve SDG target 3.2 by 2030, 11 would meet it between 2031 and 2040, 12 would meet it between 2041 and 2050, and 37 would meet it after 2050, if the average rate of decline from 1990 to 2023 continues; however, the NMR would be deteriorating in Dominica, Botswana, Grenada, Venezuela (Bolivarian Republic of), Eswatini, Zimbabwe, Dominican Republic, and Fiji.Conclusions:Despite substantial progress in reducing the NMR worldwide since 1990, more than a quarter of countries and territories would miss SDG target 3.2 by 2030, and nearly half of those countries and territories would meet SDG target 3.2 after 2050. Urgent actions are needed in most regions and countries with high NMRs, particularly those in sub-Saharan Africa.

One Size Fits All? An Assessment Tool for Solid Waste Management at Local and National Levels

Ethiopia experienced a severe food crisis and famine during 1983-85. In the peak famine year about 8 minion people were affected by famine in different parts of the country, and estimates indicate that about 1 minion people perished owing to the famine and related problems. Consequently, a number of causative factors have been proposed by different authors. Various hypotheses have been advanced to explain what went wrong in Ethiopia during the mid 1980s to result in such a devastating famine. The general view is that the food availability decline (FAD) and food entitlement famine (FEF) are the two altinative causes of the mid 1980s famine. To cast light on this baffling debate on the mid 1980s famine causation in Ethiopia, this thesis surveyed the FAD and FEF situations during the mid 1980s in Ethiopia, and the factors that led up to them. Thus, in the light of the hypotheses, this thesis considers the relative importance of the two in contributing to the mid 1980s famine. The two hypotheses are reviewed and evaluated critically. The FAD and FEF themselves result from different long-term and short-term factors by taking into account alternative approaches of assessment. This thesis presents the results of both the secondary and primary data to venture into such factors. Food production declined by 37 percent at the national level in the peak famine year of 1984.85 compared with 1982. 3. Production declined in many regions. For the Wollo region, the decline was 75 percent. These figures hint the presence of the FAD at both the national and regional levels. The more pronounced regional decline suggests the regional concentration of famine in the mid 1980s in Ethiopia. The FAD can be attributed to different long-term and short-term factors. But the underlying irony is that food production decline during the period was precipitated by recurrent significant rain failures (droughts) that affected the dominant rainfed agricultural production areas in the country. Structural problems and the problems of land and environmental degradation in the famine-prone northern highlands also reflected their influence on the long-term vulnerability to shocks. The civil war in the north seriously affected food production and distribution, particularly in those areas under rebel control. The policy apects were not favourable either. In Ethiopia the agricultural policy in the 1970s and 1980s discriminated against the dominated private peasant farms. This neglect of a dominant agricultural group contributed to the low agricultural production and productivity. The private farmers were also forced to deliver foodgrains oil a quota basis at the Government fixed low prices, whilst buying the few inputs available at relatively high prices. This worsened the private farmers terms of trade and led to a disincentive.The FEF causation aspect is discussed by focusing on the Government’s foodgrains movement and trade restriction policy, market integration, the foosgrain price trends during the famine years, and the relative price of foosgrain and other disposable assets. The empirical findings indicate that these factors were not favourable, and adversely affected the food grain supply reaching certain areas. These factors also significant contributed to eroding the purchasing power of the market dependent during the famine years and hence adversely affected the people’s food entitlement. A comparison is made between the FAD and the FEF in one of the severely affected Wollo region and its two districts. The finding is that the rate of the FEF was greater than then FAD for the peak famine year of 1984/85. But the gap between the two was not significant, indicating that the importance of both the FAD and FEF explaining Ethiopia’s famine in the mid 1980s. The presence of a structured food deficit within certain regions during the national normal production level rendered existing food production and distribution policies inadequate. The excess mortality not only casts on the success and effectiveness of public response to the famine of the mid 1980s in Ethiopia, but also increases the uncertainty about their long-term famine mitigation capabilities. The empirical results indicated that both the household coping strategies the public responses failed to prevent the food shortages from developing into famine. The political will, responsibility and prioritisation also showed food security and famine mitigation are considered at the national, region, and household levels. These scenarios basically embrace: (a) how to increase the meagre agricultural surplus at both the national and local level, and (b) how the vulnerable can get a sustainable access to any available food, when trapped in an endemic situation of insecure food supplies caused by a low resource based and the natural (environmental) constraints. Thus, we present the impact of and implication of the FAD and FEF ( and factors behind them) on the long-term tendencies in achieving the securities of food supplies.