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In the last five years, there has been an increased interest across the globe, and in the United Kingdom in particular, to define, conceptualise and measure learning gains. The concept of learning gains, briefly summarised as the improvement in knowledge, skills, work-readiness and personal development made by students during their time spent in higher education, has been hailed by some as an opportunity to measure ‘excellence’ in teaching. This chapter will review some of the common definitions and the methods employed in research on learning gains. Secondly, we will provide a critical evaluation of the computational aspects of learning gains (e.g., raw gain, normalised gain). Finally, we will critically reflect upon the lessons learnt and what is not yet known in terms of learning gains.

The purpose of this paper is to report undergraduate students’ learning gains in six areas of generic skills. The paper reports on students’ responses to the First Year Experience (FYE) Survey completed at the end of their first year and Graduating Student Survey (GSS) in the final semester of their final year.

In this study, a longitudinal design was applied in data collection, analysis and reporting of assessment if student learning gains. The undergraduate students who were the first cohort of four-year curriculum students in a Hong Kong university were selected as the sample. Repeated measures of reported learning gains of a longitudinal sample based on stacking of both FYE and GSS data were analysed using the Rasch model.

The results showed that the scale for measuring the six areas of generic skills had high reliability and good person separation. Comparison of repeated measures from the same group of students at the two time points were examined to explore whether there is growth in the generic skills during their university studies.

One limitation of the study was the relatively small sample size of 359 students in one higher education institution.

The findings of the study provide insight into conceptual understanding and measurement of university student learning gains.

Whilst several studies have investigated university student learning gains, there is limited research which explores the use Rasch modelling in assessment of student learning gains in multiple areas towards completion of their undergraduate studies.

This chapter provides a review of existing research on learning gain and related topics in higher education. The methodology adopted is a form of systematic review. The origins and meaning of learning gain, and its relation to similar terms, are discussed. The ways in which learning gain has been applied in practice and in research are considered. The issues raised by this practice and research are examined, and the various criticisms made are reviewed. Some conclusions are then drawn.

Higher education likely makes significant contributions towards planetary sustainability through its research activities, but many hope that it will also have an impact via its educational roles. International accords, national strategies and institutional commitments emphasise higher education's responsibilities with respect to education for sustainability, or for sustainable development, but research is hard-pressed to identify systematic changes in the attitudes and aspirations of young people as a consequence of the current efforts of higher education. This chapter analyses the evidence for learning gains but suggests that we should be open to the possibility of learning losses. The chapter ends by exploring if teaching students the skills and dispositions to think critically, deeply and independently, better than we do at present, might not only be a better fit to the liberal traditions and abilities of higher education but also best support generations to come to decide for themselves what their contribution to sustainability could be.

Online resources can be helpful for students and can augment the content presented in learning environments. A team consisting of four biologists, a graduate student, instructional designer and media developers collaborated on the design, development and evaluation of first-year biology online tutorials in a Canadian University. The tutorials were designed to address knowledge gaps resulting in low success rates and attrition of first-year students in biology. The decrease in the number of students in STEM has alarmed educators, prompting a call for efforts to increase STEM majors in universities. Large class sizes, such as first year biology with ∼900 registrants annually, with detail-oriented, content-heavy loads, can result in low success rates and attrition.

Active learning methods, including online formative assessments, which encourage student engagement in course material, can be effective in large introductory science classes, and thus, the authors provided engagement with tutorial online resources. The authors identified the tutorial topics by analyzing previous years' tests, student feedback and pedagogical research in undergraduate biology. The top five topics identified as common misconceptions or troublesome concepts within the course were selected. Standard instructional design processes were used to produce high-quality online tutorials. Tutorials included learning materials, videos, animations, self-assessments, reflective questions and badges to facilitate deep learning of the topics. Effectiveness of the tutorials was evaluated using quantitative methods and quasi-experimental design to compare the student learning results between the control year (without tutorials) and the year when tutorials were offered. Pre- and posttests measuring conceptual understanding were administered to assess gains in student learning. Additionally, student engagement was measured using the Classroom Survey of Student Engagement (CLASSE), and data from learning management system was collected.

Results of the study show that the tutorials were an effective means of providing supplementary assistance to students as well as fostering a gain in students' levels of engagement with the course. Data analysis indicates that there was a significant increased gain in learning of core concepts in biology. Specifically, using formative online assessments resulted in measurable learning gains in students who participated voluntarily, in comparison to students who chose not to engage in self-paced quiz testing.

As seen from the description earlier, the tutorials, and this project, provide suitable university-level complexity to address specific learning gaps in the first year course. They provide a valuable service to students in terms of representing content in an alternate format and motivating students as they engaged with videos and self-assessment most frequently. The project adds to the teaching and learning environment with respect to program design, mode of delivery and scheduling by providing self-paced tutorials that focus on specific concepts in biology. Students may review these resources whenever and as often as they feel necessary to better master the concepts. This makes the content applicable for the various preferences for approaches to learning and accommodation requirements found in students. Importantly, using formative online assessments resulted in measurable learning gains in students who participated voluntarily, in comparison to students who chose not to engage in self-paced quiz testing.

Learning gain – the attempt to measure the different ways in which students benefit from their learning experience – is now a core part of government plans for higher education across many countries. A focus on student outcomes is a key strand of enquiry for judging quality and assessing value for money. New approaches to quantifying learning gain and new metrics were developed through 13 pilot projects across England. Evaluation of the projects explored the theoretical underpinnings of the metrics including behavioural, cognitive and affective approaches, as well as progress and outcome measures, and identified challenges to measuring learning gain. In this chapter policy implications of the global accountability agenda are discussed, including the use of metrics to drive enhancement, rank excellence and ensure quality and standards.

To test the belief that only experienced librarians can effectively teach bibliographic instruction, and compare the effectiveness of active learning methods versus usual practice.

Undergraduate learning was quantitatively measured using a pre‐ and post‐assessment instrument. Two groups of library instructors, degreed librarians and graduate Library Science students participated, reporting the percentage of scripted, active‐learning instruction techniques used in classes.

There was no significant difference in undergraduate learning gains, regardless of instructor type. However, all library instructors using at least 80 percent of the scripted, active‐learning techniques showed more undergraduate learning than those instructors who did not use as many active learning techniques. In addition, undergraduate assessment showed few learning gains after participation in two library instruction sessions.

An exclusively female undergraduate population was studied.

It is not the experience of the teacher, but rather the teaching methods used that increase student learning, indicating the need for continuing professional development in this area. Additionally, two library instruction sessions, even when integrated into a course, show few student learning gains. Consequently, other instructional approaches must be considered.

Effective library instructors will be those who integrate active learning methods into their practice. Experience does not equal effectiveness.

Computational thinking (CT) is widely considered to be an important component of teaching generalizable computer science skills to all students in a range of learning environments, including robotics. However, despite advances in the design of robotics curricula that can teach CT, actual enactment in classrooms may often fail to reach this target. This study aims to understand whether the various instructional goals teachers’ hold when using these curricula may offer one potential explanation for disparities in outcomes.

In this study, the authors examine results from N = 206 middle-school students’ pre- and post-tests of CT, attitudinal surveys and surveys of their teacher’s instructional goals to determine if student attitudes and learning gains in CT are related to the instructional goals their teachers endorsed while implementing a shared robotics programming curriculum.

The findings provide evidence that despite using the same curriculum, students showed differential learning gains on the CT assessment when in classrooms with teachers who rated CT as a more important instructional goal; these effects were particularly strong for women. Students in classroom with teachers who rated CT more highly also showed greater maintenance of positive attitudes toward programming.

While there is a growing body of literature regarding curricular interventions that provide CT learning opportunities, this study provides a critical insight into the role that teachers may play as a potential support or barrier to the success of these curricula. Implications for the design of professional development and teacher educative materials that attend to teachers’ instructional goals are discussed.

Marcet, and Nicolini (2003) and Milani (2014) demonstrate within the adaptive learning framework that a forecast error-based endogenous gain mechanism that switches between constant gain and decreasing gain may be more effective than the former alone in explaining time-varying parameters. In this paper, we propose an alternative endogenous gain scheme, henceforth referred to as CEG, that is based on recent coefficient estimates by the economic agents. We then show within a controlled simulation environment that CEG outperforms both constant gain learning as well as the aforementioned switching gain algorithm in terms of mean squared forecast errors (MSFE). In addition, we demonstrate within the context of a New Keynesian model that forecasts generated under CEG perform better in certain dimensions, particularly for inflation data, compared to constant gain learning. Combined with the fact that the proposed gain scheme ports easily to existing likelihood based inferential techniques used in constant gain learning, it is readily applicable to richer, more dynamic economic models.