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This paper discusses hopeful futures for higher education and the use of technology in realising those futures through the lens of refugee education in Uganda. Through an analysis of qualitative research done with refugee students and teachers participating in a blended bridging programme designed to prepare students for entry and success in higher education, this paper explores the interplay between contingency and positions of hope for refugee students in relation to higher education and further assimilation into civic society. Two themes emerged from the analysis that suggested a prioritarian orientation for higher education to realise a more hopeful future. First, hope renders as an act of cultivation as refugee students become exposed to new concepts and develop confidence in relation to them. Second, digital technologies broaden the cultivation of hope towards greater civic participation, even if access to higher education is not achieved. Third, hope correlates to action not necessarily as a linear progressive trajectory but as a diligent watchfulness for positive possibility. The implications for higher education are that taking a prioritarian lens for refugees in educational design engages with the contingencies that impact all, placing the sector deliberately in search of positive possibility in its orientation and design. [ABSTRACT FROM AUTHOR]

Background: In this paper, we investigate the predictors for enrollment and success in Science, Technology, Engineering, and Mathematics (STEM) programs in higher education. We develop a sequential logit model in which students enroll in STEM education, may drop out from STEM higher education, or continue studying until they graduate in an STEM field. We use rich Dutch register data on student characteristics and high school exam grades to explain the differences in enrollment, success, and dropout rates. Results: We find that females are less likely to enroll in STEM-related fields, while students with higher high school mathematics grades are more likely to enroll in STEM. Female students have lower first-year dropout rates at university of applied sciences STEM programs. With respect to study success, we find that conditional on enrollment in STEM, women are less likely to graduate than men within the nominal duration or the nominal duration plus one additional year. However, female students do perform equally well as male students in terms of graduation within 10 years. Conclusions: We conclude that STEM programs are less popular among female students and that female students are less likely to graduate on time. However, females perform equally well in STEM higher education in the long run. For this reason, policy should be geared at increasing study success in terms of nominal graduation rates among female STEM students. [ABSTRACT FROM AUTHOR]

Background: In the Netherlands, there has been a strong increase in diversity among students in recent decades. Even though access for previously underrepresented groups based on economic status, ethnicity or culture has been realised to a certain extent, differences in student performance between groups persist. Research shows that teacher performance influences student achievement and that this influence is more pronounced for 'non-western students'. This creates a need for reflection on the way teachers cope with their increasingly diverse student population. This paper explores the attitudes of Dutch teachers in higher vocational education towards their diverse student population and the translation of these attitudes into teaching practice. Methods: Twenty-five teacher teams at two universities of applied sciences participated in this research. The teams came from a broad range of programmes that educate students for different future professions. A mixed method methodology was used to gather data, in which the qualitative method was most substantial. Focus group interviews on diversity and student achievement were held with each teacher team. Additionally, a questionnaire was distributed to all 274 participants, which was completed by 215 teachers. Data from the questionnaire were analysed using SPSS. In order to analyse the qualitative data we used AtlasTI. Because we applied a grounded approach, allowing teachers to form their own ideas on both diversity and student achievement, we used a similar approach in the first analytical phase. In a second phase, we compared the concepts arising from the grounded theory approach with concepts from the literature. Results and conclusions: Around 40% of the teachers repudiated the influence of diversity on student achievement and did not take student diversity into account in their teaching practice. Problems regarding the student achievement of students or groups thereof are considered as something that the students, the educational institution or society at large should cope with, not teachers themselves. Of the teachers, 60% recognised diversity among students, but mainly based on students' shortcomings and perceived problems. A minority of this 60% not only recognised but also understood diversity's effect on student achievement. Teachers do not always translate this understanding into their teaching practice. They feel they lack the skills, knowledge or time to do so. Teachers seemingly translate their understanding of diversity into their didactic and pedagogical approaches only when these conditions are met. [ABSTRACT FROM AUTHOR]

Understanding evolution is critical to learning biology, but few college instructors take advantage of the body of peer-reviewed literature that can inform evolution teaching and assessment. Here we summarize the peer-reviewed papers on tools to assess student learning of evolutionary concepts. These published concept inventories provide a resource for instructors to design courses, gauge student preparation, identify key misconceptions in their student population, and measure the impact of a lesson, course, or broader curriculum on student learning. Because these inventories vary in their format, target audience, and degree of validation, we outline and explain these features. In addition to summarizing the published concept inventories on topics within evolution, we lay out a flexible framework to help instructors decide when and how to use them. [ABSTRACT FROM AUTHOR]

In higher education, low teacher-student ratios can make it difficult for students to receive immediate and interactive help. Chatbots, increasingly used in various scenarios such as customer service, work productivity, and healthcare, might be one way of helping instructors better meet student needs. However, few empirical studies in the field of Information Systems (IS) have investigated pedagogical chatbot efficacy in higher education and fewer still discuss their potential challenges and drawbacks. In this research we address this gap in the IS literature by exploring the opportunities, challenges, efficacy, and ethical concerns of using chatbots as pedagogical tools in business education. In this two study project, we conducted a chatbot-guided interview with 215 undergraduate students to understand student attitudes regarding the potential benefits and challenges of using chatbots as intelligent student assistants. Our findings revealed the potential for chatbots to help students learn basic content in a responsive, interactive, and confidential way. Findings also provided insights into student learning needs which we then used to design and develop a new, experimental chatbot assistant to teach basic AI concepts to 195 students. Results of this second study suggest chatbots can be engaging and responsive conversational learning tools for teaching basic concepts and for providing educational resources. Herein, we provide the results of both studies and discuss possible promising opportunities and ethical implications of using chatbots to support inclusive learning. [ABSTRACT FROM AUTHOR]

Colleges across the United States have shown a commitment to advancing diversity in the STEM fields by creating programs aimed at improving outcomes of women and/or racially and ethnically minoritized students. However, most existing literature focuses on the successes of singular college programs rather than comparing these STEM interventions across the higher education landscape. This systematic review investigates the literature on diversity-focused "STEM intervention programs" (SIPs) at the postsecondary level. We categorize key features of these programs and their outcomes, and we look at which program components have the most empirical support. We examine 82 articles that reported on SIPs with disaggregated outcomes, coding each initiative's features and outcomes. Across these articles, we found six common program components, with most programs including more than one component, and five common program outcomes. Just 53 articles tested differences in outcomes of participants relative to a comparison group. This subset of research found support for the effectiveness of all coded components for improving student outcomes, though studies of multi-component programs did not parse the relative contributions of each component. Based on these findings, we conclude multi-component interventions that create a welcoming environment and focus on the successes of minoritized students help redress existing institutional shortcomings and are a promising step towards diversity, equity, and inclusion in STEM. However, more rigorous quantitative studies are needed to empirically assess the effectiveness of individual SIP program components. [ABSTRACT FROM AUTHOR]

The tremendous growth in electronic educational data creates the need to have meaningful information extracted from it. Educational Data Mining (EDM) is an exciting research area that can reveal valuable knowledge from educational databases. This knowledge can be used for many purposes, including identifying dropouts or weak students who need special attention and discovering extraordinary students who can be offered lifetime opportunities. Although former studies in EDM used an extensive range of features for predicting students' academic achievement (in terms of (i) achieved grades or (ii) passing and failing), those features are sometimes not obtainable for practical usage, and therefore, the prediction models are not feasible for employment. This study uses data mining (DM) algorithms to predict the academic performance of master' s students by using a non-extensive data set and including only the features that are easy to collect at the beginning of a studying program. To perform this study, we have collected over 700 students' records from 2010 to 2018 from the Faculty of Business Informatics and Mathematics at the University of Mannheim in Germany. Those records include demographics and post-enrollment features such as semester grades. The empirical results show the following: (i) the most significant features for predicting students' academic achievements are the students' grades in each semester (importance rate between 14 and 36%), followed by the distance from students' accommodation to university (importance rate between 6 and 18%) and culture (importance rate between 7 and 17%). On the other hand, gender, age, the numbers of failed courses, and the number of registered and unregistered exams per semester are less significant for the predictions. (ii) As expected, predictions performed after the second semester is more accurate than those performed after the first semester. (iii) Unsurprisingly, models that predict two classes yield better results than those that predict three. (iv) Random Forest classifier performs the best in all prediction models (0.77–0.94 accuracy), and using oversampling methods to deal with imbalanced data can significantly improve the performance of DM methods. For future work, we recommend testing the predictive models on other master programs and a larger datasets. Furthermore, we recommend investigating other oversampling approaches. [ABSTRACT FROM AUTHOR]

Background: Substantial research has been conducted focusing on student outcomes in mathematics courses in order to better understand the ways in which these outcomes depend on the underlying instructional methodologies found in the courses. From 2009 to 2014, the Mathematical Association of America (MAA) studied Calculus I instruction in United States (US) colleges and universities in the Characteristics of Successful Programs of College Calculus (CSPCC). One aspect of this study attempted to understand the impact of these courses on student experience.Results: In this paper, we describe results from an examination of the effect of course structure on students’ attitudes and beliefs across different versions of Calculus I at a large research university in the USA. To do this, we implemented a follow-up study of the national MAA study of calculus programs in part to identify potential relationships between various course structures and changes in attitudes and beliefs during the course. We compare our results both internally across these course structures and to the national data set.Conclusions: We find that the statistically significant changes measured in confidence and enjoyment exhibit differences across the different calculus implementations and that these changes are statistically independent of the underlying student academic backgrounds as shown by standardized test scores and high school GPA. This suggests that these observed changes in attitudes and beliefs relate to the experience in our varied course structures and not to the academic characteristics of students as they enter the course. In addition to our findings, we show how this national study can be used locally to study effects of courses on student affective traits. [ABSTRACT FROM AUTHOR]

Big data is increasingly being promoted as a game changer for the future of science, as the volume of data has exploded in recent years. Big data characterized, among others, the data comes from multiple sources, multi-format, comply to 5-V's in nature (value, volume, velocity, variety, and veracity). Big data also constitutes structured data, semi-structured data, and unstructured-data. These characteristics of big data formed "big data ecosystem" that have various active nodes involved. Regardless such complex characteristics of big data, the studies show that there exists inherent structure that can be very useful to provide meaningful solutions for various problems. One of the problems is anticipating proper action to students' achievement. It is common practice that lecturer treat his/her class with "one-size-fits-all" policy and strategy. Whilst, the degree of students' understanding, due to several factors, may not the same. Furthermore, it is often too late to take action to rescue the student's achievement in trouble. This study attempted to gather all possible features involved from multiple data sources: national education databases, reports, webpages and so forth. The multiple data sources comprise data on undergraduate students from 13 provinces in Indonesia, including students' academic histories, demographic profiles and socioeconomic backgrounds and institutional information (i.e. level of accreditation, programmes of study, type of university, geographical location). Gathered data is furthermore preprocessed using various techniques to overcome missing value, data categorisation, data consistency, data quality assurance, to produce relatively clean and sound big dataset. Principal component analysis (PCA) is employed in order to reduce dimensions of big dataset and furthermore use K-Means methods to reveal clusters (inherent structure) that may occur in that big dataset. There are 7 clusters suggested by K-Means analysis: 1. very low-risk students, 2. low-risk students, 3. moderate-risk students, 4. fluctuating-risk students, 5. high risk students, 6. very high-risk students and, 7. fail students. Among the clusters unreveal, (1) a gap between public universities and private universities across the three regions in Indonesia, (2) a gap between STEM and non-STEM programmes of study, (3) a gap between rural versus urban, (4) a gap of accreditation status, (5) a gap of quality human resources distribution, etc. Further study, we will use the characteristics of each cluster to predict students' achievement based on students' profiles, and provide solutions and interventions strategies for students to improve their likely success. [ABSTRACT FROM AUTHOR]

Background: Applied Calculus courses serve hundreds of thousands of undergraduates as quantitative preparation and gatekeepers across diverse fields of study. The current study investigated how motivational factors are associated with students' learning outcomes in Applied Calculus courses from the perspective of self-determination theory—a sound comprehensive motivation theory that has been supported by considerable research in psychology and education. In order to have a nuanced understanding of students' motivation and learning in Applied Calculus courses, we used three different types of learning measures to investigate students' mathematics achievement, including course grades, a standardized knowledge exam, and students' perceived knowledge transferability. Results: We tested the relationships between motivational factors and learning outcomes with a multi-semester sample of 3226 undergraduates from 188 Applied Calculus classrooms. To increase the precision of our analysis, we controlled for three demographic variables that are suggested to be relevant to mathematics achievement: gender, minority group status, and socioeconomic status. With a series of multilevel modeling analyses, the results reveal that: (1) competence satisfaction predicts college students' mathematics achievement over and above the satisfaction of needs for autonomy and relatedness; and (2) autonomous motivation is a more powerful predictor of college students' mathematics achievement than controlled motivation and amotivation. These findings are consistent across different types of learning outcomes. Conclusions: Self-determination theory provides an effective framework for understanding college students' motivation and learning in Applied Calculus courses. This study extends self-determination theory in the field of mathematics education and contributes to the dialogue on advancing undergraduate science, technology, engineering, and mathematics (STEM) education by providing evidence to understand how motivational factors are associated with students' learning outcomes in undergraduate mathematics courses. [ABSTRACT FROM AUTHOR]

Background: Increasingly, high dropout rates in science courses at colleges and universities have led to discussions of causes and potential support measures of students. Students' prior knowledge is repeatedly mentioned as the best predictor of academic achievement. Theory describes four hierarchically ordered types of prior knowledge, from declarative knowledge of facts to procedural application of knowledge. This study explores the relevance of these four prior knowledge types to academic achievement in the introductory phase of the two science subjects, biology and physics. Results: We assessed the knowledge types at the beginning and student achievement (measured by course completion) at the end of the first study year. We applied logistic regression models to evaluate the relationship between the knowledge types and academic achievement. First, we controlled for a well-established predictor of academic achievement (high school grade point average). Second, we added the knowledge types as predictors. For biology, we found that only knowledge about principles and concepts was a significant predictor in the first year. For physics, knowledge about concepts and principles as well as the ability to apply knowledge to problems was related to academic achievement. Conclusion: Our results concerning the knowledge types, which are of special relevance in biology and physics studies, could lead to effective measures, e.g. for identifying at-risk students and course guidance. Furthermore, the results provide a profound starting point for controlled intervention studies that systematically foster the identified relevant knowledge types in each subject and aim at a theory- and empirical-based optimization of pre- and introductory courses. [ABSTRACT FROM AUTHOR]

In almost all developed countries there has been substantial growth in university education over the last half-century. This growth has raised concerns that the benefits of university education are declining and that university education is not appropriate for students who, without the expansion, would not have been admitted. For such students, vocational education or direct entry to the labour market may be more appropriate. The purpose of this study is to examine the effects of university and vocational qualifications, net of other influences on a variety of labour market outcomes for Australian youths up to age 25; and if the benefits of university degrees differ across the achievement continuum. Achievement is measured by test scores in the OECD’s PISA assessments. The six labour market outcomes investigated are: occupational status, hourly and weekly earnings, employment, unemployment and full-time work. The study finds that university degrees provide substantially superior labour market outcomes which are not confined to high and average achievers, at least for this cohort in their formative years in the labour market. [ABSTRACT FROM AUTHOR]