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Automated guidance on essays and drawings can improve learning in precollege and college courses. Engaging students in inquiry practices is known to motivate them to persist in science, technology, engineering, and mathematics (STEM) fields and to create lifelong learners ( 1 , 2 ). In inquiry, students initiate investigations, gather data, critique evidence, and make sophisticated drawings or write coherent essays to explain complex phenomena. Yet, most instruction relies on lectures that transmit information and multiple-choice tests that determine which details students recall. Massive Open Online Courses (MOOCs) mostly offer more of the same. But new cyber-learning tools may change all this, by taking advantage of new algorithms to automatically score student essays and drawings and offer personalized guidance.

Distance education has experienced soaring development over the last decade. With millions of students in higher education enrolling in distance education, it becomes critically important to understand student learning and experiences with online education. Based on a large sample of 11,351 students taught by 1,522 instructors from 29 colleges and universities, this study investigates the factors that impact student evaluation of instruction in distance education, using a two-level hierarchical model. Key findings reveal that in a distance education setting, gender and class size are no longer significant predictors of quality of instruction. However, factors such as reasons for taking the course, student class status and instructor's academic rank have a significant impact on student evaluation of learning and instruction. Findings from this study offer important implications for institutional administrators on utilizing the evaluation results and on developing strategies to help faculty become effective online instructors.

With the pressing need for accountability in higher education, standardized outcomes assessments have been widely used to evaluate learning and inform policy. However, the critical question on how scores are influenced by students' motivation has been insufficiently addressed. Using random assignment, we administered a multiple-choice test and an essay across three motivational conditions. Students' self-report motivation was also collected. Motivation significantly predicted test scores. A substantial performance gap emerged between students in different motivational conditions (effect size as large as .68). Depending on the test format and condition, conclusions about college learning gain (i.e., value added) varied dramatically from substantial gain (d = 0.72) to negative gain (d = -0.23). The findings have significant implications for higher education stakeholders at many levels.

Evaluation of the effectiveness of higher education has received unprecedented attention from stakeholders at many levels. The Voluntary System of Accountability (VSA) is one of the initiatives to evaluate institutional core educational outcomes (e.g., critical thinking, written communication) using standardized tests. As promising as the VSA method is for calculating a valueadded score and allowing results to be comparable across institutions, it has a few potential methodological limitations. This study proposed an alternative way of value-added computation which takes advantage of multilevel models and considers important institution-level variables. The institutional value-added ranking was significantly different for some of the institutions (i.e., from being ranked at the bottom to performing better than 50% of the institutions) between these two methods, which may lead to substantially different consequences for those institutions, should the results be considered for accountability purposes.

Universities contribute to economic growth and national competitiveness by equipping students with higher-order thinking and academic skills. Despite large investments in university science, technology, engineering and mathematics (STEM) education, little is known about how the skills of STEM undergraduates compare across countries and by institutional selectivity. Here, we provide direct evidence on these issues by collecting and analysing longitudinal data on tens of thousands of computer science and electrical engineering students in China, India, Russia and the United States. We find stark differences in skill levels and gains among countries and by institutional selectivity. Compared with the United States, students in China, India and Russia do not gain critical thinking skills over four years. Furthermore, while students in India and Russia gain academic skills during the first two years, students in China do not. These gaps in skill levels and gains provide insights into the global competitiveness of STEM university students across nations and institutional types. This large-scale study tracing tens of thousands of university students in China, India, Russia and the United States finds significant differences in skill levels and gains among countries.

We assess and compare computer science skills among final-year computer science undergraduates (seniors) in four major economic and political powers that produce approximately half of the science, technology, engineering, and mathematics graduates in the world. We find that seniors in the United States substantially outperform seniors in China, India, and Russia by 0.76–0.88 SDs and score comparably with seniors in elite institutions in these countries. Seniors in elite institutions in the United States further outperform seniors in elite institutions in China, India, and Russia by ∼0.85 SDs. The skills advantage of the United States is not because it has a large proportion of high-scoring international students. Finally, males score consistently but only moderately higher (0.16–0.41 SDs) than females within all four countries.

This study investigates current practices in how admissions policies are communicated through student-facing web pages. One hundred fifty web pages (30 institutions, 5 admissions web pages each, stratified by degree-level and major) were scraped for information about holistic admissions policies and required application materials. Overall, more holistic language was used in undergraduate web pages than graduate web pages, particularly among nonminority serving institutions (MSIs). Graduate web pages required more application materials than undergraduate web pages, with more materials required for graduate web pages that used holistic language. Additionally, undergraduate web pages that used holistic language identified more qualitative cutoffs (e.g., identifying relative importance of application components, in-depth discussion of how factors are balanced) than the web pages that did not use holistic language. The paper concludes with a discussion of directions for future research and of best practices and practical takeaways for admissions officers to help increase equity in admissions.

The HEIghten Critical Thinking Test (HCTT), developed by the Educational Testing Service (ETS), has gained traction both in the United States (US) and elsewhere. This study presents preliminary validity evidence for the use of the HCTT in Ireland. We provide evidence of the HCTT’s overall structure, reliability, and relationships with student-level variables. While some item discrimination and reliability indices are not optimal, results of other analyses support the low stakes use of the HCTT in an Irish context. The procedures and outcomes of the study will be of interest to those planning to validate an existing high quality measure of critical thinking in local contexts.

Learning outcomes assessment has been widely used by higher education institutions both nationally and internationally. One of its popular uses is to document learning gains of students. Prior studies have recognized the potential imbalance between freshmen and seniors in terms of their background characteristics and their prior academic performance and have used linear regression adjustments for these differences, which some researchers have argued are not fully adequate. We explored an alternative adjustment via propensity score weighting to balance the samples on background variables including SAT score, gender, and ethnicity. Results involving cross-sectional sample of freshmen and seniors from seven groups of majors within a large research university showed that students in most of the majors demonstrated significant learning gain. Additionally, there was a slight difference in learning gain rankings across major groupings when compared to multiple regression results.

Parallel analysis has been well documented to be an effective and accurate method for determining the number of factors to retain in exploratory factor analysis. The O’Connor (2000) procedure for parallel analysis has many benefits and is widely applied, yet it has a few shortcomings in dealing with missing data and ordinal variables. To address these technical issues, we adapted and modified the O’Connor procedure to provide an alternative method that better approximates the ordinal data by factoring in the frequency distributions of the variables (e.g., the number of response categories and the frequency of each response category per variable). The theoretical and practical differences between the modified procedure and the O’Connor procedure are discussed. The SAS syntax for implementing this modified procedure is also provided.