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Purpose The purpose of this paper is to develop a web-based interactive learning object (ILO) of introductory Computer Science (CS) concept on recursion and compare two feedback methods in the learning assessment part. Design/methodology/approach Test driven development (TDD) approach was used to develop ILO. The authors adapted Multimedia Educational Resource for Learning and Online Teaching (MERLOT) standard instrument to evaluate ILO’s effectiveness as an e-learning tool. Three respondents, from a list of pre-identified prospective evaluators, were randomly chosen and served as raters for MERLOT, while 32 student-respondents coming from first-year Math and CS undergraduate majors were randomly assigned to each ILO version implementing either one of the two feedback methods. Findings ILO obtained mean ratings above 4 (in scale 1-5) in three MERLOT criteria, namely, potential effectiveness as teaching tool, ease of use, and quality of content, which is rated highest (mean=4.40, SD=0.53). The study also revealed that immediate feedback increases retention while delayed feedback improves generating new knowledge. Respondents who viewed the ILO implementing immediate feedback in their first session had statistically significantly higher scores (mean=8.25, SD=0.80) than those who viewed with delayed feedback (mean=7.63, SD=0.89). In their second session, the same observation was noted although with higher mean scores. These results give evidence that the developed ILO met standards in e-learning material and showed evidence of its effectiveness with preferably implementing immediate feedback. Research limitations/implications Although the developed ILO can now be used in school as supplementary learning material in teaching the concept of recursion in an introductory CS subject, a pilot testing of the web-based ILO using a larger sample of respondents to validate its effectiveness for online distance learning educational material can be pursued. Furthermore, in designing and creating an ILO, the provision of feedback during the assessment stage is necessary for effecting learning. Originality/value The study was a first to develop ILO for CS topic on recursion. The paper also compared which of two known feedback methods is best to implement in an ILO.

Combining filters in an ensemble to improve feature selection performance is a growing field in the literature. Current techniques, however, are focused on approaches that suffer from drawbacks such as sensitivity to skewed distribution, among others. To address this gap, this paper investigates the applicability of multiple criteria decision-making in ensemble feature selection. This paper adopts the Evaluation based on Distance from Average Solution (EDAS) method due to its many familiar elements to the feature selection community. An experiment was performed on six datasets and a control group. The paper uses the six datasets as levels of the blocking factor. A negative control group (i.e., no feature selection) was adopted to compare with the proposed algorithm. Results show that the proposed ensemble FS algorithm was able to reduce the dataset without compromising the performance of the classifier. The findings in this study would contribute to the literature in several ways. First, the paper is one of the few works to demonstrate how MCDM can be used in feature selection with promising results. Second, this paper is one of the few works to demonstrate the significance of including datasets as levels of a blocking factor when performing significance testing. Finally, this paper is the first to demonstrate the applicability of EDAS as an ensemble FS algorithm. As such, the findings in this paper could spark the cross-fertilization of feature selection and MCDM.