The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups. In this ongoing discussion, fears and realities are often indistinguishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution's learning support by implementing data and analytics with a view on improving student success. In this paper, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.
This paper explores a longitudinal approach to combining engagement, performance and social connectivity data from a MOOC using the framework of exponential random graph models (ERGMs). The idea is to model the social network in the discussion forum in a given week not only using performance (assignment scores) and overall engagement (lecture and discussion views) covariates within that week, but also on the same person-level covariates from adjacent previous and subsequent weeks. We find that over all eight weekly sessions, the social networks constructed from the forum interactions are relatively sparse and lack the tendency for preferential attachment. By analyzing data from the second week, we also find that individuals with higher performance scores from current, previous, and future weeks tend to be more connected in social network. Engagement with lectures had significant but sometimes puzzling effects on social connectivity. However, the relationships between social connectivity, performance, and engagement weakened over time, and results were not stable across weeks.
Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system designed to provide researchers with insights regarding data logged from randomized controlled experiments conducted within the ASSISTments TestBed. The Assessment of Learning Infrastructure (ALI) builds upon existing technologies to foster a symbiotic relationship beneficial to students, researchers, the platform and its content, and the learning analytics community. ALI is a sophisticated automated reporting system that provides an overview of sample distributions and basic analyses for researchers to consider when assessing their data. ALI's benefits can also be felt at scale through analyses that crosscut multiple studies to drive iterative platform improvements while promoting personalized learning.
Journal writing is an important and common reflective practice in education. Students' reflection journals also offer a rich source of data for formative assessment. However, the analysis of the textual reflections in class of large size presents challenges. Automatic analysis of students' reflective writing holds great promise for providing adaptive real time support for students. This paper proposes a method based on topic modeling techniques for the task of themes exploration and reflection grade prediction. We evaluated this method on a sample of journal writings from pre-service teachers. The topic modeling method was able to discover the important themes and patterns emerged in students' reflection journals. Weekly topic relevance and word count were identified as important indicators of their journal grades. Based on the patterns discovered by topic modeling, prediction models were developed to automate the assessing and grading of reflection journals. The findings indicate the potential of topic modeling in serving as an analytic tool for teachers to explore and assess students' reflective thoughts in written journals.
This paper presents an analytics dashboard that has been developed for designers of interactive e-books. This is part of the EU-funded MC Squared project that is developing a platform for authoring interactive educational e-books. The primary objective is to develop technologies and re- sources that enhance creative thinking for both designers (authors) and learners. The learning material is expected to offer learners opportunities to engage creatively with mathematical problems and develop creative mathematical think- ing. The analytics dashboard is designed to increase authors' awareness so that they can make informed decisions on how to redesign and improve the e-books. This paper presents architectural and design decisions on key features of the dashboard, and discusses the evaluation of a high- fidelity prototype. We discuss our future steps and some findings related to use of the dashboard for exploratory data analysis that we believe generalise to similar work.
Wheel-spinning is the phenomenon where students, in spite of repeated practice, make no progress towards mastering a skill. Prior research has shown that a considerable number of students can get stuck in the mastery learning cycle--unable to master the skill despite the affordances of the educational software. In such situations, the tutor's promise of 'infinite practice' via mastery learning becomes more a curse than a blessing. Prior research on wheel spinning overlooks two aspects: how much time is spent wheel spinning and the problem of imbalanced data. This work provides an estimate of the amount of time students spend wheel spinning. A first-cut approximation is that 24% of student time in the ASSISTments system is spent wheel spinning. However, the data used to train the wheel spinning model were imbalanced, resulting in a bias in the model's predictions causing it to undercount wheel spinning. We identify this misprediction as an issue for model extrapolation as a general issue within EDM, provide an algebraic workaround to modify the detector's predictions to better accord to reality, and show that students spend approximately 28% of their time wheel spinning in ASSISTments.
We present our approach to designing and evaluating tools that can assist teachers in classroom settings where students are using Exploratory Learning Environments (ELEs), using as our case study the MiGen system, which targets 11-14 year old students' learning of algebra. We discuss the challenging role of teachers in exploratory learning settings and motivate the need for visualisation and notification tools that can assist teachers in focusing their attention across the whole class and inform teachers' interventions. We present the design and evaluation approach followed during the development of MiGen's Teacher Assistance tools, drawing parallels with the recently proposed LATUX workflow but also discussing how we go beyond this to include a large number of teacher participants in our evaluation activities, so as to gain the benefit of different view points. We present and discuss the results of the evaluations, which show that participants appreciated the capabilities of the tools and were mostly able to use them quickly and accurately.
Since LAK2015 an increasing number of researchers are taking learning design into consideration when predicting learning behavior and outcomes across different modules. Learning design is widely studied in the Higher Education sector, but few studies have empirically connected learning designs of a substantial number of courses with learning behavior in Learning Management Systems (LMSs) and learning performance. This study builds on preliminary learning design work that was presented at LAK2015 by the Open University UK. In this study we linked 151 modules and 111.256 students with students' behavior (
Prerequisite skill structures have been closely studied in past years leading to many data-intensive methods aimed at refining such structures. While many of these proposed methods have yielded success, defining and refining hierarchies of skill relationships are often difficult tasks. The relationship between skills in a graph could either be causal, indicating a prerequisite relationship (skill A must be learned before skill B), or non-causal, in which the ordering of skills does not matter and may indicate that both skills are prerequisites of another skill. In this study, we propose a simple, effective method of determining the strength of pre-to-post-requisite skill relationships. We then compare our results with a teacher-level survey about the strength of the relationships of the observed skills and find that the survey results largely confirm our findings in the data-driven approach.
Given the importance of reading proficiency and habits for young students, an online e-quiz bank, Reading Battle, was launched in 2014 to facilitate reading improvement for primary-school students. With more than ten thousand questions in both English and Chinese, the system has attracted nearly five thousand learners who have made about half a million question answering records. In an effort towards delivering personalized learning experience to the learners, this study aims to discover potentially useful knowledge from learners' reading and question answering records in the Reading Battle system, by applying association rule mining and clustering analysis. The results show that learners could be grouped into three clusters based on their self-reported reading habits. The rules mined from different learner clusters can be used to develop personalized recommendations to the learners. Implications of the results on evaluating and further improving the Reading Battle system are also discussed.