The influence of students’ epistemic orientations on their learning behavior and outcomes is well-documented. However, limited research explores students’ epistemic orientations in terms of conceptual engagement and learning outcomes. This study, set within the context of higher education, examined the patterns of conceptual engagement among two performance groups and identifies differences in their epistemic orientations. Both epistemic network analysis (ENA) and ordered network analysis (ONA) methods were used. The results from the ENA revealed distinct trajectories and patterns of conceptual engagement between high-performing and low-performing students during different periods in their learning journey. High-performing students were able to establish a more interconnected and distributed epistemic network earlier than their low-performing counterparts. ONA results revealed that (1) high-performing students were more inclined to employ abstract theoretical concepts to address empirical concerns, doing so more frequently and earlier; and (2) low-performing students benefitted from forum interactions with high-performing students to expand their knowledge resources and engagement with theoretical constructs over time. These discoveries contribute to our comprehension of epistemic orientations in different learners. The implications of this study could help generate learning analytics that monitor students’ conceptual engagement in forum discussion and provide feedback to guide the design of learning.

Affected by the Covid-19 pandemic, the way adolescents receive their education has changed drastically from offline classrooms to online digital space. Despite the benefits of digital devices, we must also be cautious of the possible negative impacts of using digital devices excessively. In this study, we proposed a smart planning course to support adolescents in managing daily digital device usage. Meanwhile, we examined the effects of this course through a novel multimodal learning analytics (MMLA) approach. Although results of the quasi-experiment indicated few significant effects of the intervention, possibly due to its timing, the proposed MMLA approach was shown to provide more comprehensive and refined data compared to traditional methods. Future studies can use this approach for further activity-based analysis of students’ digital well-being.

The rapid development of wearable technologies has dramatically promoted the potential usages of wearable devices in educational data analytics. However, the large amount of input data and the various types of educational output labels also increase the difficulties in selecting the useful information and discovering the implicit relations between different input data. To address this issue, this paper proposed a new two-layer approach for conducting educational data analytics automatically. In this approach, there are three key components: input layer, output layer and recognition model. For the input layer, we adopted the newly proposed optimization algorithm: Adaptive Multi-Population Optimization (AMPO) to select the most related input features and suitable model structures. For the output layer, we inserted domain-specific constraints during the searching for all combinations of different output labels to discover a meaningful output strategy with a relatively higher accuracy. Based on the input elements and output strategy provided by the input layer and the output layer, the recognition model will produce the corresponding recognition accuracy. With these three components, our proposed method can find out some connotative information to provide guidance for conducting educational data analytics and drawing meaningful conclusions.

With the rapid development in wearable technology, wearable devices integrating with various sensors have been broadly applied in different areas. Yet there is seldom any previous study which focuses on applying wearable devices and deep learning in learning analytics. This paper considers a sophisticated real-time learning analytics platform for analyzing students' learning states and learning activities with wearable devices and deep learning. During the experimental period of this platform, students will receive instant notifications from an intelligent mobile application when their heart rate are out of their normal range so that the actual learning activities conducted by students can be collected to train deep learning models for recognizing their learning activities. At the same time, students can enjoy the sleeping monitoring and the exercise monitoring functionalities provided by the smart watches in this platform. The results of the interviews conducted after the experiment for this platform demonstrate that 89% of students think that this platform is useful for their daily lives and 65% of students report that this platform brings positive effects on their learning in different aspects. More importantly, this work sheds lights on the possibility of applying wearable devices in learning analytics to improve the learning effectivenesses and life qualities of students.

A goal of learning analytics is to inform and improve learning design. Previous studies have attempted to interpret learners' clickstream data based on learning science theories. Many of these interpretations are made without reference to the specific learning designs of the courses being analyzed. Here, we report on a learning design informed analytics exploration of an introductory MOOC on Computer Science and Python programming. The learning resources (videos) and practice resources (short exercises and problem sets) are analyzed according to the knowledge types and cognitive process levels respectively, both based on a revised Bloom's Taxonomy. A heat map visualization of the access intensity on a learner resource access transition matrix and social network analysis are used to analyze learners' behavior with respect to the different resource categories. The results show distinctively different patterns of access between groups of students with different course performance and different academic backgrounds.

With the popularity of wearable devices, smart watches containing various sensors have been widely adopted for many healthcare applications. Yet there is rarely any research study on the possible uses of smart watches for learning analytics, particularly for analyzing students' learning activities through the physiological and/or movement data collected on their smart watches. This paper considers a pioneering and sophisticated learning analytics platform using fine-tuned deep learning models to predict students' learning activities based on the real-time data, including their heart rates, calories, three-axis accelerometer and gyroscope data, captured on wearable devices and then uploaded onto a cloud server for thorough analyses. To validate on the actual activities conducted by each student, an intelligent mobile application is developed to push instant notifications for students to report their own activities whenever the change of heart rates are deviated significantly from their normal values. Based on students' heart rates and calories, a long-short term memory (LSTM) model is built to classify students' learning states as active or not with an impressive prediction accuracy of 95% whereas another hybrid model combining both the LSTM and convolutional neural networks attains the highest prediction accuracy of 74% to predict students' specific learning activities as based on their physiological and movement data. The prototype implementation clearly demonstrates the feasibility of the proposed framework for learning analytics. More importantly, this work shed lights on various directions including the integration of noise filters to preprocess the collected data for further investigation.

Student learning activity in MOOCs can be viewed from multiple perspectives. We present a new organization of MOOC learner activity data at a resolution that is in between the fine granularity of the clickstream and coarse organizations that count activities, aggregate students or use long duration time units. A detailed access trajectory (DAT) consists of binary values and is two dimensional with one axis that is a time series, and the other that is a chronologically ordered list of a MOOC component type's instances, videos in instructional order, for example. Most popular MOOC platforms generate data that can be organized as detailed access trajectories (DATs). We explore the value of DATs by conducting four empirical mini-studies. Our studies suggest DATs contain rich information about students' learning behaviors and facilitate MOOC learning analyses.

We investigate learner efficiency by categorizing a computational MOOC and analyzing user behavior data from a learning design point of view. Learning design is important both when designing courses as well as studying them. Learning behavior can be observed from the MOOC platform data. For this study we ask two learning designer experts to categorize a course on MITx: "6.00.1x Introduction to Computer Science and Programming Using Python". We use these categorizations to investigate relationships with learning behavior by analyzing the MOOC platform data. Our study verifies that learning design can be correlated to learning behavior, e.g. students exhibit a pattern of behavior associated to a component's difficulty and category.

Notions of what constitutes quality in design in traditional on-campus or online teaching and learning may not always translate into scaled digital environments. The DesignLAK17 workshop builds on the DesignLAK16 workshop to explore one aspect of this theme, namely the opportunities arising from the use of analytics in scaled assessment design. New paradigms for learning design are exploiting the distinctive characteristics and potentials of analytics, trace data and newer kinds of sensory data usable on digital platforms to transform assessment. But, characteristics of quality assessment design need to be reconsidered, and new metrics for capturing quality are required. This symposium and workshop focuses on what might be appropriate quality metrics and indicators for assessment design in scaled learning. It aims to build a community of interest round the topic, to share perspectives, and to generate design and research ideas.

Ecological concepts, and in particular population dynamics, has been found to be among the most difficult topics in biology. Some researchers pointed to students' relatively weak mathematical background as the main source of learning difficulties. This paper reports on an investigation of pupil's (n=73) reasoning about the ecological phenomena by using an iconic modelling tool, WorldMaker. The simulations eliminated the need for understanding of mathematical equations, and made the ecological concepts much more accessible to some children. However, many of the pupils reasoned from an anthropocentric perspective that obstructed their ability to predict ecological phenomena which requires systems thinking.

Difficulties in the teaching and learning of scientific concepts and theories have been well documented in many areas of science, including the physical and life sciences, over the last two decades. Different psychological theories have been put forward as explanatory models for such difficulties. However, another perspective for understanding such learning difficulties and consequently to better support learning is to understand the relationship between the theories and the phenomena they explain. Often, scientific theories involve the postulation of explanatory models that operate at an entirely different level (or scale) in terms of the magnitude and time span from the natural phenomena that it seeks to explain. This paper elaborates the challenges to learning that this poses and introduces how an iconic modeling tool, WorldMaker, can scaffold conceptualization in the learning of a variety of scientific theories.