{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:09:13Z","timestamp":1764688153470,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute for the Future of Education at Tecnol\u00f3gico de Monterrey"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The growing implementation of digital platforms and mobile devices in educational environments has generated the need to explore new approaches for evaluating the learning experience beyond traditional self-reports or instructor presence. In this context, the NPFC-Test dataset was created from an experimental protocol conducted at the Experiential Classroom of the Institute for the Future of Education. The dataset was built by collecting multimodal indicators such as neuronal, physiological, and facial data using a portable EEG headband, a medical-grade biometric bracelet, a high-resolution depth camera, and self-report questionnaires. The participants were exposed to a digital test lasting 20 min, composed of audiovisual stimuli and cognitive challenges, during which synchronized data from all devices were gathered. The dataset includes timestamped records related to emotional valence, arousal, and concentration, offering a valuable resource for multimodal learning analytics (MMLA). The recorded data were processed through calibration procedures, temporal alignment techniques, and emotion recognition models. It is expected that the NPFC-Test dataset will support future studies in human\u2013computer interaction and educational data science by providing structured evidence to analyze cognitive and emotional states in learning processes. In addition, it offers a replicable framework for capturing synchronized biometric and behavioral data in controlled academic settings.<\/jats:p>","DOI":"10.3390\/data10070103","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T03:54:28Z","timestamp":1751255668000},"page":"103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["NPFC-Test: A Multimodal Dataset from an Interactive Digital Assessment Using Wearables and Self-Reports"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0362-1641","authenticated-orcid":false,"given":"Luis","family":"Mor\u00e1n-Mirabal","sequence":"first","affiliation":[{"name":"Tecnologico de Monterrey, Institute for the Future of Education, Av. Eugenio Garza Sada 2501 Sur, Tecnol\u00f3gico, Monterrey 64700, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0712-8758","authenticated-orcid":false,"given":"Luis","family":"G\u00fcemes-Frese","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, Institute for the Future of Education, Av. Eugenio Garza Sada 2501 Sur, Tecnol\u00f3gico, Monterrey 64700, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5245-9634","authenticated-orcid":false,"given":"Mariana","family":"Favarony-Avila","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, Institute for the Future of Education, Av. Eugenio Garza Sada 2501 Sur, Tecnol\u00f3gico, Monterrey 64700, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5324-459X","authenticated-orcid":false,"given":"Sergio","family":"Torres-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, Institute for the Future of Education, Av. Eugenio Garza Sada 2501 Sur, Tecnol\u00f3gico, Monterrey 64700, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2181-7645","authenticated-orcid":false,"given":"Jessica","family":"Ruiz-Ramirez","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, Institute for the Future of Education, Av. Eugenio Garza Sada 2501 Sur, Tecnol\u00f3gico, Monterrey 64700, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lang, C., Wise, A.F., Siemens, G., Ga\u0161evi\u0107, D., and Merceron, A. (2022). Multimodal Learning Analytics\u2014Rationale, Process, Examples, and Direction. Handbook of Learning Analytics, Society for Learning Analytics Research (SoLAR). [2nd ed.]. Chapter 6.","DOI":"10.18608\/hla22.001"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1007\/s42438-020-00155-y","article-title":"Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity","volume":"2","author":"Rapanta","year":"2020","journal-title":"Postdigital Sci. Educ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cebral-Loureda, M., Rinc\u00f3n-Flores, E., and Sanchez-Ante, G. (2023). Using AI for Educational Research in Multimodal Learning Analytics. What the AI Can Do: Knowledge Strengths, Biases and Resistances to Assume the Algorithmic Culture, Taylor & Francis Group. [1st ed.]. Chapter 9.","DOI":"10.1201\/b23345"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mu, S., Cui, M., and Huang, X. (2020). Multimodal Data Fusion in Learning Analytics: A Systematic Review. Sensors, 20.","DOI":"10.3390\/s20236856"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100136","DOI":"10.1016\/j.caeai.2023.100136","article-title":"Research trends in multimodal learning analytics: A systematic mapping study","volume":"4","author":"Ouhaichi","year":"2023","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_6","unstructured":"Liu, Y., Peng, S., Song, T., Zhang, Y., Tang, Y., and Li, Z. (2022). Multi-Modal Emotion Recognition Based on Local Correlation Feature Fusion. Front. Neurosci., 16."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Horvers, A., Tombeng, N., Bosse, T., Lazonder, A.W., and Molenaar, I. (2021). Emotion Recognition Using Wearable Sensors: A Review. Sensors, 21.","DOI":"10.3390\/s21237869"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mor\u00e1n-Mirabal, L.F., Ruiz-Ram\u00edrez, J.A., Gonz\u00e1lez-Grez, A.A., Torres-Rodr\u00edguez, S.N., and Ceballos, H. (2025, January 22\u201325). Applying the Living Lab Methodology for Evidence-Based Educational Technologies. Proceedings of the 2025 IEEE Global Engineering Education Conference, EDUCON 2025, London, UK.","DOI":"10.1109\/EDUCON62633.2025.11016413"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rojas Vistorte, A.O., Deroncele-Acosta, A., Mart\u00edn Ayala, J.L., Barrasa, A., L\u00f3pez-Granero, C., and Mart\u00ed-Gonz\u00e1lez, M. (2024). Integrating artificial intelligence to assess emotions in learning environments: A systematic literature review. Front. Psychol., 15.","DOI":"10.3389\/fpsyg.2024.1387089"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lian, H., Lu, C., Li, S., Zhao, Y., Tang, C., and Zong, Y. (2023). A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face. Entropy, 25.","DOI":"10.3390\/e25101440"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Christenson, S.L., Reschly, A.L., and Wylie, C. (2012). Academic Emotions and Student Engagement. Handbook of Research on Student Engagement, Springer.","DOI":"10.1007\/978-1-4614-2018-7"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bustos-L\u00f3pez, M., Cruz-Ram\u00edrez, N., Guerra-Hern\u00e1ndez, A., S\u00e1nchez-Morales, L.N., Cruz-Ramos, N.A., and Alor-Hern\u00e1ndez, G. (2022). Wearables for Engagement Detection in Learning Environments: A Review. Biosensors, 12.","DOI":"10.3390\/bios12070509"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s12008-021-00760-6","article-title":"Biometric applications in education","volume":"15","author":"Escobar","year":"2021","journal-title":"Int. J. Interact. Des. Manuf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Mustieles, M.A., Lima-Carmona, Y.E., Pacheco-Ram\u00edrez, M.A., Mendoza-Armenta, A.A., Romero-G\u00f3mez, J.E., Cruz-G\u00f3mez, C.F., Rodr\u00edguez-Alvarado, D.C., and Arceo, A. (2024). Wearable Biosensor Technology in Education: A Systematic Review. Sensors, 24.","DOI":"10.20944\/preprints202403.0831.v1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105111","DOI":"10.1016\/j.compedu.2024.105111","article-title":"Bridging Computer and Education Sciences: A Systematic Review of Automated Emotion Recognition in Online Learning Environments","volume":"220","author":"Yu","year":"2024","journal-title":"Comput. Educ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Apicella, A., Arpaia, P., Frosolone, M., Improta, G., Moccaldi, N., and Pollastro, A. (2022). EEG-based measurement system for monitoring student engagement in learning 4.0. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-09578-y"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Boothe, M., Yu, C., Lewis, A., and Ochoa, X. (2022, January 21\u201325). Towards a Pragmatic and Theory-Driven Framework for Multimodal Collaboration Feedback. Proceedings of the LAK22: 12th International Learning Analytics and Knowledge Conference, New York, NY, USA.","DOI":"10.1145\/3506860.3506898"},{"key":"ref_18","unstructured":"Mirabal, L.F.M., \u00c1lvarez, L.M.M., and Ramirez, J.A.R. (2025, June 18). Muse 2 Headband Specifications (Neuronal Tracking), Reporte, ITESM, 2024. Available online: https:\/\/hdl.handle.net\/11285\/685108."},{"key":"ref_19","unstructured":"Mor\u00e1n Mirabal, L.F., Favaroni Avila, M., and Ruiz Ramirez, J.A. (2024). Empatica Embrace Plus Specifications (Physiological Tracking), ITESM. Available online: https:\/\/hdl.handle.net\/11285\/685107."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/JSSC.2014.2364270","article-title":"A 0.13 \u03bcm CMOS System-on-Chip for a 512 \u00d7 424 Time-of-Flight Image Sensor with Multi-Frequency Photo-Demodulation up to 130 MHz and 2 GS\/s ADC","volume":"50","author":"Bamji","year":"2015","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_21","unstructured":"Microsoft Corporation (2023). Azure Kinect Developer Kit Documentation, Microsoft Docs. Available online: https:\/\/learn.microsoft.com\/en-us\/azure\/kinect-dk\/."},{"key":"ref_22","unstructured":"(2025, June 18). Mind Monitor. Available online: https:\/\/mind-monitor.com\/."},{"key":"ref_23","unstructured":"Mor\u00e1n Mirabal, L.F., G\u00fcemes Frese, L.E., Favarony Avila, M., Torres Rodr\u00edguez, S.N., and Ruiz Ramirez, J.A. (2024). NPFC-Test 23A: A Dataset for Assessing Neuronal, Physiological, and Facial Coding Attributes in a Human-Computer Interaction Learning Scenario, Tecnol\u00f3gico de Monterrey."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/7\/103\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:44:44Z","timestamp":1761057884000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/7\/103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":23,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["data10070103"],"URL":"https:\/\/doi.org\/10.3390\/data10070103","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2025,6,30]]}}}