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Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.

This paper aims to explore advances in indirect personality assessment, with emphasis on the psychology of digital behavior based on the analysis of new technological devices and platforms for interpersonal relationships, identifying – along the way – those findings that may be useful to carry out a reconstructive psychological assessment (RPA) of applicability in the legal context.

Different fields of knowledge are explored, transferring the findings to the field of psychology of digital behavior, analyzing the publications that report findings on the analysis of new technological devices and platforms for interpersonal relationships and identifying – along the way – those findings that may result useful to carry out an RPA of applicability in the legal context.

The application of RPA represents a significant advance in the integration of criminal psychology and forensic technology in legal contexts, opening new fields of action for forensic psychology.

The article has transferred advances in computer science to the field of forensic psychology, with emphasis on the relevance of RPA (from the analysis of digital behavioral residues) in the interpretation of behavioral evidence for the indirect evaluation of the personality and within the judicial context (when the victim and/or accused are not included).

This study aims to investigate the causal complexity of ECF investors’ peer effect through two different paths of structural social influence.

Using the fuzzy-set qualitative comparative analysis (fsQCA) approach, we employ 157 samples from a Chinese ECF source to explore how peer-effect are caused by both informational and normative mechanisms.

The findings suggests that there are multiple configurations could lead to ECF investors’ high level peer-effect through both informational and normative mechanisms, and the informational mechanism' role depends on the normative mechanism, while the normative mechanism could lead to peer-effect independently.

The findings enrich the literature on ECF investors’ behaviors by revealing the diverse configurations resulting in investors’ peer-effect and shedding new light on investigating the decision-making driven by information asymmetry and relationship settings for individuals at a disadvantage.

This is the first study that investigates the multiple-driven of ECF investors’ decision-making and the importance of mutual norms in individuals' decision-making by complex network analysis approach and qualitative comparative analysis from the perspective of complexity. The results reveal the complexity of investors’ decision-making in ECF.