Enhancing learning effectiveness requires one to define suitable learning outcomes and align assessment constructs with these outcomes. We present a quad-faceted feature-based graph network to classify assessment texts into domain-agnostic class labels more accurately. The proposed model incorporates four complementary graphs (syntactic, semantic, sequential, and topical) with observable and latent node types and unique edge weight computations that are dependent on node properties to extract unique features from a given text. The purpose of incorporating syntactic information is to consider the dependency parsing between word nodes, while the semantic information is to provide the algorithm with contextual similarity between phrase nodes that are more effective than words in encapsulating the meaning of a text. The sequential graph is applied to regular expression nodes that contribute to a domain-agnostic class label, while the topical graph identifies topics that are convergent to each other based on their distributions. As opposed to existing techniques that construct graphs solely based on word nodes, the proposed model exploits the benefits of term weighting, nested phrases, regular expressions, and topic modeling to develop a diverse heterogeneous architecture for text classification. We evaluate the classification performance on questions with different class labels such as cognitive complexities, reasoning capabilities, and question types, as well as longer documents. Experiment results show that the proposed model outperforms in terms of macroaverage F1 score when compared with existing deep learning techniques. We also demonstrate the application of the classification model to understand learners’ attitudes via an empirical study in a workplace-learning environment.

The need to cultivate creativity in engineering education calls for opportunities for students to exercise freedom in proposing and pursuing projects aligned with their interests. This paper presents insights into an undergraduate makerspace-based course in terms of factors affecting students’ creativity-related self-efficacy. We conducted a survey on students who come from an engineering and science background to gain their opinions about the impact of this course on enhancing their creativity. To establish if there is a significant difference in the students’ creativity, we performed the non-parametric Wilcoxon signed-rank test comparing the first and second survey, with results showing that there is a statistically significant increase in students’ creativity-related self-efficacy. There was a general increase for all the items, especially students’ perceptions toward the relevance of the course, the conduciveness of the learning environment, opportunities to make and learn from mistakes, and the resourcefulness of their team. Results obtained via quantitative statistical analysis was backed up by qualitative analysis that employed text mining techniques such as automatic key phrase extraction and sentiment analysis on the open-ended responses and the reason(s) for the Likert-scale answer choice. In addition, we used the Spearman’s rho to report correlations between Likert-scale items and determine the variables that are significantly and positively correlated with the creativity-related self-efficacy construct. A multivariate regression model was then constructed to observe the extent to which each highly correlated variable impacts creativity-related self-efficacy; of which, a sense of relevance appears to have the largest effect. Through gaining insights into the factors that may impact students’ creativity-related self-efficacy, this study contributed to a deeper understanding on how this important attribute could be developed through a makerspace-based university course.

This Innovative Practice Full Paper presents experiences in designing a student-led virtual freshmen orientation program that uses a Minecraft environment. We describe the planning process, roles of the organizing committee members, and how the game was constructed for participants to learn and interact with one another. The student organizers not only created a virtual environment that scales the college map where more than a hundred freshmen (participants) could have an immersive experience of the campus, but also ensured the branding and marketing, logistics, and safety/well-being aspects of the event. In this paper, we present students’ experience of this program from both the designers’ as well as the participants’ perspectives. We conducted surveys with the organizing committee members and interviewed the participants to gain insights on their perception of this event. Our analysis showed that student organizers had the autonomy to brainstorm, suggest creative ideas, develop novel games, and procure materials. They also felt that they developed authentic programming and leadership skills. On the other hand, participants felt engaged as the event was well-organized, had clear delivery of information, introduced them to new technology, made them more familiar with the campus, provided a conducive environment to hone their soft skills such as communication and teamwork even before they officially enrolled as undergraduate students in an engineering program, and helped them establish social networks to support them throughout their undergraduate education journey.

Classification of questions according to domain-agnostic class labels relies on a suitable feature extraction process. We propose the use of phrases that are more effective than words to represent questions. The proposed phrase-based topic modeling technique employs asymmetric priors that are scaled with a new C-value for nested regular expressions. In addition, to suppress high-frequency words in phrases, we deploy term weightages computed using the modified distinguishing feature selector. The proposed approach also incorporates a new topic regularization mechanism to facilitate efficient mapping of questions to class labels. We validate the performance of the above approach via four datasets across different domain-agnostic class labels comprising question types, reasoning capabilities, and cognitive complexities. Results obtained highlight that the proposed technique outperforms existing methods in terms of macro-average F1 score.

To deliver on the potential outcome-based teaching and learning holds for engineering education, it is important for engineering courses to provide students with different types of deliberate practice opportunities that align to the program's learning outcomes. Working from these requirements, we increased the design and measurement intentionality of a digital signal processing (DSP) course. To align the course's learning outcomes more constructively with its assessment measures, we automated the process of classifying DSP questions according to learning outcomes by introducing a model that integrates topic modeling and machine learning. In this work, we explored the effect of pre-processing procedures in terms of stopword selection and word co-occurrence redundancy issue in question classification inferences. In this work, we proposed a customized variant of the Word Network Topic Model, q-WNTM, which is able to use its pre-classified DSP questions to reliably classify new questions according to the course's learning outcomes.