Interpretable predictive modeling of non-stationary long time series DOI

Dunwang Qin,

Zhen Peng,

Lifeng Wu

и другие.

Computers & Industrial Engineering, Год журнала: 2024, Номер 194, С. 110412 - 110412

Опубликована: Июль 22, 2024

Язык: Английский

Linking social media data and patents via Wikipedia for social problem-solving R&D DOI
Seung‐Hyun Lee, Jiho Lee, Jae-Min Lee

и другие.

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 111039 - 111039

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Predicting learning performance using NLP: an exploratory study using two semantic textual similarity methods DOI

Charalampos Papadimas,

Vasiliki Ragazou, Ilias Karasavvidis

и другие.

Knowledge and Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

0

A Critical Review of Using Learning Analytics for Formative Assessment: Progress, Pitfalls and Path Forward DOI
Seyyed Kazem Banihashem, Dragan Gašević, Omid Noroozi

и другие.

Journal of Computer Assisted Learning, Год журнала: 2025, Номер 41(3)

Опубликована: Май 5, 2025

ABSTRACT Background While formative assessment is widely regarded as essential for improving teaching and learning, it remains difficult to operationalize due systemic misalignment with other instructional practices, limited teacher capacity, low feedback quality, inferential uncertainty, domain‐general approaches, validity concerns. Objectives This editorial introduces a special issue that critically examines how learning analytics can contribute advancing by addressing persistent challenges in its design implementation. Results Conclusion The twelve studies featured this demonstrate several innovations such adaptive feedback, multimodal analytics, predictive modeling, dashboard design, evidence‐centered frameworks. Collectively, these enhance personalizing scaling dialogic understanding the nature of validity, automating assessment, uncovering deeper patterns, alignment goals. However, also highlights underexplored gaps, including disciplinary adaptation tools, lack ongoing student involvement insufficient attention ethical concerns physiological motivational dimensions role emerging technologies, particular, Generative AI (GenAI). argues more critical, inclusive, context‐sensitive approach assessment—one centers pedagogy, agency, long‐term educational value. contributions lay groundwork future research, policy, practice aimed at transforming through analytics.

Язык: Английский

Процитировано

0

Interpretable predictive modeling of non-stationary long time series DOI

Dunwang Qin,

Zhen Peng,

Lifeng Wu

и другие.

Computers & Industrial Engineering, Год журнала: 2024, Номер 194, С. 110412 - 110412

Опубликована: Июль 22, 2024

Язык: Английский

Процитировано

2