Alireza Ahadi

Advances in Information Technology (IT) and computer science have without a doubt had a significant impact on our daily lives. The past few decades have witnessed the advancement of IT enabled processes in generating actionable insights in various fields, encouraging research based applications of modern Data Science methods. Among many other fields, education research has also been adopting different analytical approaches to advance the state of education systems. Moreover, developments in software engineering and web-based applications have made collection of education data possible at large scales. This systematic review aims to explore the 21st century’s state of the art applications of text mining methods used in the field of education. We analyse the metadata of all publications that use text mining or natural language processing in educational settings to report on the key themes of application of text mining methods in educational studies providing an overview of the current state of the art and the future directions for research and applications.

Teacher professional learning workshops have been frequently used to prepare in-service and pre-service teachers for effective use of technology in education. Evaluation of these workshops is crucial to identify the effectiveness of these programmes in terms of improving teaching skills, increasing knowledge, changing attitudes, and developing capabilities which support the achievement of student learning outcomes. Multiple approaches for evaluating professional development programmes have been developed, though each with different emphases and theoretical positioning. In this systematic review, 41 research-oriented teacher professional development workshops on technology use were critically analysed to understand how such workshops are evaluated. This study examines content evaluation, instrument types, and common professional development frameworks in teacher professional development workshops and reports on their usage and alignment. Based on the findings, the majority of papers in the systematic review did not deploy comprehensive professional development evaluation models to assess teacher professional development workshops. The majority of studies did not report on the use of established instruments for the purposes of data collection. It was further observed that the majority of these studies did not attempt to evaluate different dimensions of teacher change or different dimensions related to evaluation.

As COVID-19 continues to impact upon education worldwide, systems and organizations are rapidly transiting their professional learning to online mode. This raises concerns, not simply about whether online professional learning can result in equivalent outcomes to face-to-face learning, but more importantly about how to best evaluate online professional learning so we can iteratively improve our approaches. This case study analyses the evaluation of an online teacher professional development workshop for the purpose of critically reflecting upon the efficacy of workshop evaluation techniques. The evaluation approach was theoretically based in a synthesis of six seminal workshop evaluation models, and structured around eight critical dimensions of educational technology evaluation. The approach involving collection of pre-workshop participant background information, pre-/post-teacher perceptions data, and post-workshop focus group perceptions, enabled the changes in teacher knowledge, skills, and beliefs to be objectively evaluated, at the same time as providing qualitative information to effectively improve future iterations of the workshops along a broad range of dimensions. The evaluation approach demonstrated that the professional learning that was shifted into online mode in response to COVID-19 could unequivocally result in significant improvements to professional learning outcomes. More importantly, the evaluation approach is critically contrasted with previous evaluation models, and a series of recommendations for the evaluation of technology-enhanced teacher professional development workshops are proposed.

The regulation of tumor suppressor genes by microRNAs (miRNAs) is often demonstrated as a one-miRNA-to-one-target relationship. However, given the large number of miRNA sites within a 3′ untranslated region (UTR), most targets likely undergo miRNA cooperation or combinatorial action. Programmed cell death 4 (PDCD4), an important tumor suppressor, prevents neoplastic events and is commonly downregulated in cancer. This study investigates the relationship between miRNA 21 (miR-21) and miR-499 in regulating PDCD4. This was explored using miRNA overexpression, mutational analysis of the PDCD4 3′ UTR to assess regulation at each miRNA site, and 50% inhibitory concentration (IC₅₀) calculations for combinatorial behavior. We demonstrate that the first miR-499 binding site within PDCD4 is inactive, but the two remaining sites are both required for PDCD4 suppression. Additionally, the binding of miR-21 to PDCD4 influenced miR-499 activity through an increase in its silencing potency and stabilization of its mature form. Furthermore, adjoining miRNA sites more than 35 nucleotides (nt) apart could potentially regulate thousands of 3′ UTRs, similar to that observed between miR-21 and miR-499. The regulation of PDCD4 serves as a unique example of regulatory action by multiple miRNAs. This relationship was predicted to occur on thousands of targets and may represent a wider mode of miRNA regulation.

We describe a method for analyzing student data from online programming exercises. Our approach uses contingency tables that combine whether or not a student answered an online exercise correctly with the number of attempts that the student made on that exercise. We use this method to explore the relationship between student performance on online exercises done during semester with subsequent performance on questions in a paper-based exam at the end of semester. We found that it is useful to include data about the number of attempts a student makes on an online exercise.

Course instructors need to be able to identify students in need of assistance as early in the course as possible. Recent work has suggested that machine learning approaches applied to snapshots of small programming exercises may be an effective solution to this problem. However, these results have been obtained using data from a single institution, and prior work using features extracted from student code has been highly sensitive to differences in context. This work provides two contributions: first, a partial reproduction of previously published results, but in a different context, and second, an exploration of the efficacy of neural networks in solving this problem. Our findings confirm the importance of two features (the number of steps required to solve a problem and the correctness of key problems), indicate that machine learning techniques are relatively stable across contexts (both across terms in a single course and across courses), and suggest that neural network based approaches are as effective as the best Bayesian and decision tree methods. Furthermore, neural networks can be tuned to be reliably pessimistic, so they may serve a complementary role in solving the problem of identifying students who need assistance.