Evidence map›Paper›PMID 42787819›Full record

ArticleFrontiers in psychology2026

Motivation and learning strategies as predictors of academic maladjustment among first-year engineering students.

Gabriela Monica Biriș, Ana-Maria Cazan, Simona Nicoleta Neagu

Abstract read
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Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gabriela Monica BirișRomanian Academy, Institute of Philosophy and Psychology "Constantin Rădulescu-Motru", Bucharest, Romania.
Ana-Maria CazanRomanian Academy, Institute of Philosophy and Psychology "Constantin Rădulescu-Motru", Bucharest, Romania.
Simona Nicoleta NeaguThe Department of Teacher Training and Social Sciences, The National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study investigates the relationships between academic motivation, learning strategies, and academic maladjustment among first-year undergraduate students at a science and technology university, with the aim of understanding factors that influence the transition to the university environment. Methods: A total of 383 first-year students from the National University of Science and Technology POLITEHNICA Bucharest participated in the study. Data were collected online using questionnaires assessing reasons for attending university, learning strategies, and academic maladjustment, including procrastination, test anxiety, lack of honesty, Machiavellian attitudes, neuroticism, and somatization. Correlational analyses and multiple regression analyses were conducted to examine the relationships between these variables. Results: Expectations and Lack of Alternatives/Inertia emerged as consistent predictors of academic maladjustment, whereas Personal-Intellectual Development, Career/Materialism, and Humanitarian orientations showed limited independent effects. Learning strategies made a relatively small contribution to maladjustment after controlling for motivation. When examined independently, concrete/practical strategies were negatively associated with maladjustment, whereas memorization was positively associated with maladjustment, although these associations were weak. Discussion: The findings suggest that academic motivation may play a more important role than learning strategies in the academic adjustment of first-year students. The results support the value of integrated interventions combining motivation assessment, counseling, and the development of effective learning strategies to support students' autonomy, academic adjustment, and social integration.

Indexed as

academic maladjustmentacademic motivationfirst-year studentshigher technical educationlearning strategies

Identifiers

PMID42787819
PMCPMC13602208

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.