An Empirical Study of Adaptive Feedback to Enhance Cognitive Ability in Programming Learning among College Students: A Perspective Based on Multimodal Data Analysis
Restricted accessResearch articleFirst published online June, 2025
An Empirical Study of Adaptive Feedback to Enhance Cognitive Ability in Programming Learning among College Students: A Perspective Based on Multimodal Data Analysis
Cognitive ability is closely associated with the acquisition of programming skills, and enhancing learners’ cognitive ability is a crucial factor in improving the efficacy of programming education. Adaptive feedback strategies can provide learners with personalized support based on their learning context, which helps to stimulate their interest and improve learning outcomes. Nevertheless, it remains unclear whether adaptive feedback can enhance the cognitive ability of programming learners. This study applies adaptive feedback strategies to an introductory programming course by designing a quasi-experiment to analyze and reveal the effects of adaptive feedback on the cognitive ability to program learners from multiple modalities, including physiological, psychological, and behavioral data. Sixty-five first-year university students were randomly assigned to either experimental or control groups. The experimental group received adaptive feedback during the programming learning process and the control group received non-differential feedback. The findings demonstrated that adaptive feedback significantly enhances the cognitive ability of programming learners. The experimental group demonstrated superior performance in cognitive ability tests, programming examinations, and programming self-efficacy. Furthermore, adaptive feedback was found to markedly improve the cognitive processing of learners, as evidenced by the amplitude and latency of the P300 component in EEG signals, key-press reaction times and accuracy rates.
AhrariG.GargariR. B.AbdollahiS.PorkarP.YousefiH. (2018). Prediction of creativity based on students’ brain executive function. Shenakht Journal of Psychology and Psychiatry, 5(3), 66–80. https://doi.org/10.29252/shenakht.5.3.66
2.
Al-AbyadhM. H. A.Abdel AzeemH. A. H. (2022). Academic achievement: Influences of university students’ self-management and perceived self-efficacy. Journal of Intelligence, 10(3), 55. https://doi.org/10.3390/jintelligence10030055
3.
AlexanderJ. E.PorjeszB.BauerL. O.KupermanS.MorzoratiS.O’ConnorS. J.PolichJ. (1995). P300 hemispheric amplitude asymmetries from a visual oddball task. Psychophysiology, 32(5), 467–475. https://doi.org/10.1111/j.1469-8986.1995.tb02098.x
4.
Alia-KleinN.Preston-CampbellR. N.KimS. W.ParetoD.LoganJ.WangG.-J.BiegonA. (2020). Human cognitive ability is modulated by aromatase availability in the brain in a sex-specific manner. Frontiers in Neuroscience, 14, 565668. https://doi.org/10.3389/fnins.2020.565668
5.
ArroyoI.WoolfB. P.BurelsonW.MuldnerK.RaiD.TaiM. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. International Journal of Artificial Intelligence in Education, 24(4), 387–426. https://doi.org/10.1007/s40593-014-0023-y
6.
AstutiN. H.RusilowatiA.SubaliB. (2021). STEM-based learning analysis to improve students’ problem solving abilities in science subject: A literature review. Journal of Innovative Science Education, 10(1), 79–86. https://doi.org/10.15294/jise.v9i2.38505
7.
AzevedoR. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84–94. https://doi.org/10.1080/00461520.2015.1004069
8.
BaranaA.FissoreC.MarchisioM. (2020). From standardized assessment to automatic formative assessment for adaptive teaching. Paper presented at the Proceedings of the 12th International Conference on Computer Supported Education (pp. 285–296). https://doi.org/10.5220/0009577302850296
9.
BaranaA.MarchisioM.SacchetM. (2021). Effectiveness of automatic formative assessment for learning mathematics in higher education. In 7th International Conference on Higher Education Advances (HEAd’21) (pp. 1–8). Editorial Universitat Politècnica de València. https://doi.org/10.4995/head21.2021.13030
10.
BarrotJ. S. (2023). Using automated written corrective feedback in the writing classrooms: Effects on L2 writing accuracy. Computer Assisted Language Learning, 36(4), 584–607. https://doi.org/10.1080/09588221.2021.1936071
11.
BernackiM. L.GreeneM. J.LobczowskiN. G. (2021). A systematic review of research on personalized learning: Personalized by whom, to what, how, and for what purpose (s)?Educational Psychology Review, 33(4), 1675–1715. https://doi.org/10.1007/s10648-021-09615-8
12.
BersM. U.FlanneryL.KazakoffE. R.SullivanA. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72(1), 145–157. https://doi.org/10.1016/j.compedu.2013.10.020
13.
BerssanetteJ. H.de FranciscoA. C. (2021). Cognitive load theory in the context of teaching and learning computer programming: A systematic literature review. IEEE Transactions on Education, 65(3), 440–449. https://doi.org/10.1109/TE.2021.3127215
14.
BerssanetteJ. H.de FranciscoA. C. (2021). Active learning in the context of the teaching/learning of computer programming: A systematic review. Journal of Information Technology Education: Research, 20, 201–220. https://doi.org/10.28945/4767
15.
BrandtN. D.LechnerC. M.TetznerJ.RammstedtB. (2020). Personality, cognitive ability, and academic performance: Differential associations across school subjects and school tracks. Journal of Personality, 88(2), 249–265. https://doi.org/10.1111/jopy.12482
16.
BrownG. T.PetersonE. R.YaoE. S. (2016). Student conceptions of feedback: Impact on self‐regulation, self‐efficacy, and academic achievement. British Journal of Educational Psychology, 86(4), 606–629. https://doi.org/10.1111/bjep.12126
17.
ButlerD. L.WinneP. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281. https://doi.org/10.3102/00346543065003245
18.
CarrollJ. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press.
19.
ChenX.XieH.ZouD.HwangG.-J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1(3), 100002. https://doi.org/10.1016/j.caeai.2020.100002
20.
ChrysafiadiK.TroussasC.VirvouM. (2022). Personalised instructional feedback in a mobile-assisted language learning application using fuzzy reasoning. International Journal of Learning Technology, 17(1), 53–76. https://doi.org/10.1504/ijlt.2022.10048511
21.
ÇiftciS.BildirenA. (2020). The effect of coding courses on the cognitive abilities and problem-solving skills of preschool children. Computer Science Education, 30(1), 3–21. https://doi.org/10.1080/08993408.2019.1696169
de Masson D’AutumeC.RuderS.KongL.YogatamaD. (2019). Episodic memory in lifelong language learning. Advances in Neural Information Processing Systems, 32.
24.
DemirayakP.Kıyıİ.İşbitirenY. Ö.YenerG. (2023). Cognitive load associates prolonged P300 latency during target stimulus processing in individuals with mild cognitive impairment. Scientific Reports, 13(1), 15956. https://doi.org/10.1038/s41598-023-43132-8
DonchinE. (1979). Event-related brain potentials: A tool in the study of human information processing. In Evoked brain potentials and behavior (pp. 13–88). Springer. https://doi.org/10.1007/978-1-4684-3462-0_2
27.
FinleyA. J.AngusD. J.KnightE. L.van ReekumC. M.LachmanM. E.DavidsonR. J.SchaeferS. M. (2024). Resting EEG periodic and aperiodic components predict cognitive decline over 10 years. Journal of Neuroscience, 44(13), e1332232024. https://doi.org/10.1523/jneurosci.1332-23.2024
28.
GabbayH.CohenA. (2022). Investigating the effect of automated feedback on learning behavior in MOOCs for programming. International Educational Data Mining Society.
29.
GamlemS. M.KvingeL. M.SmithK.EngelsenK. S. (2019). Developing teachers’ responsive pedagogy in mathematics, does it lead to short-term effects on student learning?Cogent Education, 6(1), 1676568. https://doi.org/10.1080/2331186x.2019.1676568
30.
GehlotL. (2021). Cognitive development by zone of proximal development (ZPD). Journal of Education, Culture and Society, 12(2), 432–444. https://doi.org/10.15503/jecs2021.2.432.444
31.
HaldemanG.TjangA.Babeş-VromanM.BartosS.ShahJ.YuchtD.NguyenT. D. (2018). Providing meaningful feedback for autograding of programming assignments. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (pp. 278–283). ACM. https://doi.org/10.1145/3159450.3159502
32.
HansenneM. (2000). The p300 cognitive event-related potential. I. Theoretical and psychobiologic perspectives. Clinical Neurophysiology, 30(4), 191–210. https://doi.org/10.1016/s0987-7053(00)00223-9
33.
HaoQ.WilsonJ. P.OttawayC.IriumiN.ArakawaK.SmithD. H. (2019). Investigating the essential of meaningful automated formative feedback for programming assignments. In 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (pp. 151–155). IEEE. https://doi.org/10.1109/vlhcc.2019.8818922
34.
HattieJ.GanM. (2011). Instruction based on feedback. In Handbook of research on learning and instruction (pp. 263–285). Routledge. https://doi.org/10.4324/9780203839089.ch13
35.
HocineN. (2019). Personalized serious games for self-regulated attention training. Paper presented at the Adjunct publication of the 27th conference on user modeling, adaptation and personalization. https://doi.org/10.1145/3314183.3323458
36.
HooshyarD.MalvaL.YangY.PedasteM.WangM.LimH. (2021). An adaptive educational computer game: Effects on students’ knowledge and learning attitude in computational thinking. Computers in Human Behavior, 114(6), 106575. https://doi.org/10.1016/j.chb.2020.106575
37.
IglesiasA.MartínezP.AlerR.FernándezF. (2009). Learning teaching strategies in an adaptive and intelligent educational system through reinforcement learning. Applied Intelligence, 31(1), 89–106. https://doi.org/10.1007/s10489-008-0115-1
38.
JiangB.LiZ. (2021). Effect of Scratch on computational thinking skills of Chinese primary school students. Journal of Computers in Education, 8(4), 505–525. https://doi.org/10.1007/s40692-021-00190-z
39.
JohnsonC. I.PriestH. A. (2014). The feedback principle in multimedia learning. The Cambridge Handbook of Multimedia Learning, 449–463. https://doi.org/10.1017/cbo9781139547369.023
40.
Juárez-RamírezR.JiménezS.Tapia-IbarraV. (2019). What is programming? Putting all together, Part II–the cognitive skills associated. Paper presented at the 2019 7th International Conference in Software Engineering Research and Innovation. https://doi.org/10.1109/conisoft.2019.00018
41.
KadarR.WahabN. A.OthmanJ.ShamsuddinM.MahlanS. B. (2021). A study of difficulties in teaching and learning programming: A systematic literature review. International Journal of Academic Research in Progressive Education and Development, 10(3), 591–605. https://doi.org/10.6007/ijarped/v10-i3/11100
42.
KaleliogluF.GülbaharY. (2014). The effects of teaching programming via scratch on problem solving skills: A discussion from learners’ perspective. Informatics in Education, 13(1), 33–50. https://doi.org/10.15388/infedu.2014.03
43.
KarimA.ProulxM. J.de SousaA. A.LikovaL. T. (2021). Neuroplasticity and crossmodal connectivity in the normal, healthy brain. Psychology & neuroscience, 14(3), 298. https://doi.org/10.1037/pne0000258
KorkmazÖ.AltunH. (2014). A validity and reliability study of the attitude scale of computer programming learning (ASCOPL). Online Submission, 4(1), 30–43. https://doi.org/10.13054/mije.13.73.4.1
KosmynaN.MaesP. (2019). AttentivU: An EEG-based closed-loop biofeedback system for real-time monitoring and improvement of engagement for personalized learning. Sensors, 19(23), 5200. https://doi.org/10.3390/s19235200
48.
KumarB. P.ScholarP.RaoM. V. (2020). Personalized affective feedback to address student’s frutration in ITS. Complexity International, 24(1), 87–97.
49.
KutasM.McCarthyG.DonchinE. (1977). Augmenting mental chronometry: The P300 as a measure of stimulus evaluation time. Science, 197(4305), 792–795. https://doi.org/10.1126/science.887923
50.
LahatD.AdalT.JuttenC. (2015). Multimodal data fusion: An overview of methods, challenges and prospects. Proceedings of the IEEE, 103(9), 1449–1477. https://doi.org/10.1109/jproc.2015.2460697
51.
LeeM. K.EvansM. (2019). Investigating the operating mechanisms of the sources of L2 writing self‐efficacy at the stages of giving and receiving peer feedback. The Modern Language Journal, 103(4), 831–847. https://doi.org/10.1111/modl.12598
52.
LeeS.MattesonA.HooshyarD.KimS.JungJ.NamG.LimH. (2016). Comparing programming language comprehension between novice and expert programmers using EEG analysis. Paper presented at the 2016 IEEE 16th international conference on bioinformatics and bioengineering (BIBE). https://doi.org/10.1109/bibe.2016.30
53.
LehmannT.HähnleinI.IfenthalerD. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32, 313–323. https://doi.org/10.1016/j.chb.2013.07.051
54.
LiH.LiX.WangX. (2017). On cognitive competence difference between urban and rural students - an empirical study based on the reports of five counties in yunnan province. Educational Research, 38(7), 115–121.
55.
LiP.ZhangY.LiuS.LinL.ZhangH.TangT.GaoD. (2023). An EEG-based brain cognitive dynamic recognition network for representations of brain fatigue. Applied Soft Computing, 146(5), 110613. https://doi.org/10.1016/j.asoc.2023.110613
56.
LiW.TanR.XingY.LiG.LiS.ZengG.PiD. (2022). A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks. Scientific Data, 9(1), 481. https://doi.org/10.1038/s41597-022-01557-2
57.
LiangX.HeJ.ZhouJ.LiuP. (2020). The influence of cognitive ability on academic performance of junior middle school students: A mediated moderation model. Psychological Development and Education, 36(4), 449–461.
58.
LingH.-C.ChiangH.-S. (2022). Learning performance in adaptive learning systems: A case study of web programming learning recommendations. Frontiers in Psychology, 13, 770637. https://doi.org/10.3389/fpsyg.2022.770637
59.
Luxton-ReillyA. (2016). Learning to program is easy. Paper presented at the Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education. https://doi.org/10.1145/2899415.2899432
60.
MaglieroA.BashoreT. R.ColesM. G.DonchinE. (1984). On the dependence of P300 latency on stimulus evaluation processes. Psychophysiology, 21(2), 171–186. https://doi.org/10.1111/j.1469-8986.1984.tb00201.x
61.
MahatanankoonP.WolfJ. (2021). Cognitive learning strategies in an introductory computer programming course. Information Systems Education Journal, 19(3), 11–20.
62.
MaiorH. A.WilsonM. L.SharplesS. (2018). Workload alerts—using physiological measures of mental workload to provide feedback during tasks. ACM Transactions on Computer-Human Interaction, 25(2), 1–30. https://doi.org/10.1145/3173380
63.
MangaroskaK.Martinez‐MaldonadoR.VesinB.GaševićD. (2021). Challenges and opportunities of multimodal data in human learning: The computer science students’ perspective. Journal of Computer Assisted Learning, 37(4), 1030–1047. https://doi.org/10.1111/jcal.12542
64.
MangaroskaK.SharmaK.GaševicD.GiannakosM. (2020). Multimodal learning analytics to inform learning design: Lessons learned from computing education. Journal of Learning Analytics, 7(3), 79–97. https://doi.org/10.18608/jla.2020.73.7
65.
MangaroskaK.SharmaK.GaševićD.GiannakosM. (2022). Exploring students’ cognitive and affective states during problem solving through multimodal data: Lessons learned from a programming activity. Journal of Computer Assisted Learning, 38(1), 40–59. https://doi.org/10.1111/jcal.12590
66.
MarchisioM.BaranaA.FioraveraM.RabellinoS.ConteA. (2018). A model of formative automatic assessment and interactive feedback for STEM. Paper presented at the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). https://doi.org/10.1109/compsac.2018.00178
67.
Martinez-MaldonadoR.EcheverriaV.Fernandez-NietoG.YanL.ZhaoL.AlfredoR.GaševićD. (2023). Lessons learnt from a multimodal learning analytics deployment in-the-wild. ACM Transactions on Computer-Human Interaction, 31(1), 1–41. https://doi.org/10.1145/3622784
68.
MarwanS.AkramB.BarnesT.PriceT. W. (2022). Adaptive immediate feedback for block-based programming: Design and evaluation. IEEE Transactions on Learning Technologies, 15(3), 406–420. https://doi.org/10.1109/tlt.2022.3180984
69.
McCarthyG.DonchinE. (1981). A metric for thought: A comparison of P300 latency and reaction time. Science, 211(4477), 77–80. https://doi.org/10.1126/science.7444452
70.
MhashiM. M.AlakeelA. (2013). Difficulties facing students in learning computer programming skills at Tabuk University. Recent Advances in Modern Educational Technologies, 1, 15–24.
71.
MoryE. H. (2013). Feedback research revisited. In Handbook of research on educational communications and technology (pp. 738–776). Routledge.
72.
MutangaM. B. (2020). The effect of cognitive factors in determining students’ success in computer programming. Journal of Theoretical and Applied Information Technology, 98(17), 3607–3618.
73.
NamK. W.KimH. J.LeeS. (2019). Connecting plans to action: The effects of a card-coded robotics curriculum and activities on Korean kindergartners. The Asia-Pacific Education Researcher, 28(5), 387–397. https://doi.org/10.1007/s40299-019-00438-4
74.
NarcissS.SosnovskyS.SchnaubertL.AndrèsE.EichelmannA.GoguadzeG.MelisE. (2014). Exploring feedback and student characteristics relevant for personalizing feedback strategies. Computers & Education, 71, 56–76. https://doi.org/10.1016/j.compedu.2013.09.011
75.
NashR. A.WinstoneN. E.GregoryS. E.PappsE. (2018). A memory advantage for past-oriented over future-oriented performance feedback. Journal of Experimental Psychology: Learning, Memory, and Cognition, 44(12), 1864. https://doi.org/10.1037/xlm0000549
76.
NehraR.GroverS.ChetriD.SoodA.DasC. P. (2012). P300 latency and neurocognitive functioning in recently diagnosed human immunodeficiency virus patients. Indian journal of psychological medicine, 34(4), 376–380. https://doi.org/10.4103/0253-7176.108225
77.
NobleS. C.WoodsE.WardT.RingwoodJ. V. (2024). Accelerating P300-based neurofeedback training for attention enhancement using iterative learning control: A randomised controlled trial. Journal of Neural Engineering, 21(2), 026006. https://doi.org/10.1088/1741-2552/ad2c9e
78.
OchoaX.WorsleyM. (2016). Augmenting learning analytics with multimodal sensory data. Journal of Learning Analytics, 3(2), 213–219. https://doi.org/10.18608/jla.2016.32.10
79.
PengmaneeS. (2016). Developing students’ mathematical reasoning ability based on constructivist approach. Journal of Advances in Humanities and Social Sciences, 2(4), 221–231. https://doi.org/10.20474/jahss-2.4.3
80.
PezzinoM. (2018). Online assessment, adaptive feedback and the importance of visual learning for students. The advantages, with a few caveats, of using MapleTA. International Review of Economics Education, 28(1), 11–28. https://doi.org/10.1016/j.iree.2018.03.002
PratC. S.MadhyasthaT. M.MottarellaM. J.KuoC. H. (2020). Relating natural language aptitude to individual differences in learning programming languages. Scientific Reports, 10(1), 3817. https://doi.org/10.1038/s41598-020-60661-8
83.
PsycharisS.KalliaM. (2017). The effects of computer programming on high school students’ reasoning skills and mathematical self-efficacy and problem solving. Instructional Science, 45(5), 583–602. https://doi.org/10.1007/s11251-017-9421-5
84.
QianY.LehmanJ. D. (2019). Using targeted feedback to address common student misconceptions in introductory programming: A data-driven approach. Sage Open, 9(4), 2158244019885136. https://doi.org/10.1177/2158244019885136
85.
RahateA.WalambeR.RamannaS.KotechaK. (2022). Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions. Information Fusion, 81, 203–239. https://doi.org/10.1016/j.inffus.2021.12.003
86.
RavenJ. (2000). The raven’s progressive matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 1–48. https://doi.org/10.1006/cogp.1999.0735
87.
ReisR.SoaresG.MongioviM.AndradeW. L. (2019). Evaluating feedback tools in introductory programming classes. In 2019 IEEE frontiers in education conference (FIE) (pp. 1–7). IEEE. https://doi.org/10.1109/fie43999.2019.9028418
88.
SailerM.BauerE.HofmannR.KiesewetterJ.GlasJ.GurevychI.FischerF. (2023). Adaptive feedback from artificial neural networks facilitates pre-service teachers’ diagnostic reasoning in simulation-based learning. Learning and Instruction, 83, 101620. https://doi.org/10.1016/j.learninstruc.2022.101620
89.
SainiN.BhardwajS.AgarwalR. (2020). Classification of EEG signals using hybrid combination of features for lie detection. Neural Computing & Applications, 32(8), 3777–3787. https://doi.org/10.1007/s00521-019-04078-z
90.
SalisburyD. F.ShentonM. E.McCarleyR. W. (1999). P300 topography differs in schizophrenia and manic psychosis. Biological Psychiatry, 45(1), 98–106. https://doi.org/10.1016/s0006-3223(98)00208-x
91.
SavilleP. D.BrayS. R.GinisK. A. M.CairneyJ.Marinoff-ShupeD.PettitA. (2014). Sources of self-efficacy and coach/instructor behaviors underlying relation-inferred self-efficacy (RISE) in recreational youth sport. Journal of Sport & Exercise Psychology, 36(2), 146–156. https://doi.org/10.1123/jsep.2013-0144
92.
SchifferA.-M.SilettiK.WaszakF.YeungN. (2017). Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning. NeuroImage, 146, 626–641. https://doi.org/10.1016/j.neuroimage.2016.08.057
93.
SharmaK.GiannakosM. (2020). Multimodal data capabilities for learning: What can multimodal data tell us about learning?British Journal of Educational Technology, 51(5), 1450–1484. https://doi.org/10.1111/bjet.12993
94.
SharmaK.NiforatosE.GiannakosM.KostakosV. (2020a). Assessing cognitive performance using physiological and facial features: Generalizing across contexts. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1–41. https://doi.org/10.1145/3411811
95.
SharmaK.PapamitsiouZ.OlsenJ. K.GiannakosM. (2020). Predicting learners’ effortful behaviour in adaptive assessment using multimodal data. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 480–489). https://doi.org/10.1145/3375462.3375498
96.
SheH. C.LiaoY. W. (2010). Bridging scientific reasoning and conceptual change through adaptive web‐based learning. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 47(1), 91–119. https://doi.org/10.1002/tea.20309
StudenovaA.ForsterC.EngemannD. A.HenschT.SandersC.MaucheN.NikulinV. (2023). Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG. Elife, 12, RP88367. https://doi.org/10.7554/elife.88367.1
99.
SwellerJ.CooperG. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59–89. https://doi.org/10.1207/s1532690xci0201_3
ThompsonT. W.WaskomM. L.GarelK.-L. A.Cardenas-IniguezC.ReynoldsG. O.WinterR.AlvarezG. A. (2013). Failure of working memory training to enhance cognition or intelligence. PLoS One, 8(5), e63614. https://doi.org/10.1371/journal.pone.0063614
102.
TikhomirovaT.MalykhA.MalykhS. (2020). Predicting academic achievement with cognitive abilities: Cross-sectional study across school education. Behavioral Sciences, 10(10), 158. https://doi.org/10.3390/bs10100158
103.
TroussasC.KrouskaA.SgouropoulouC. (2021). A novel teaching strategy through adaptive learning activities for computer programming. IEEE Transactions on Education, 64(2), 103–109. https://doi.org/10.1109/te.2020.3012744
104.
UgwuanyiC. S.OkekeC. I.AgedaT. A. (2020). Motivation and self-efficacy as predictors of learners’ academic achievement. Journal of Sociology and Social Anthropology, 11(3–4), 215–222. https://doi.org/10.31901/24566764.2020/11.3-4.351
105.
UllahZ.LajisA.JamjoomM.AltalhiA. H.ShahJ.SaleemF. (2019). A rule-based method for cognitive competency assessment in computer programming using Bloom’s taxonomy. IEEE Access, 7, 64663–64675. https://doi.org/10.1109/access.2019.2916979
106.
WambsganssT.NiklausC.CettoM.SöllnerM.HandschuhS.LeimeisterJ. M. (2020). AL: An adaptive learning support system for argumentation skills. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–14). https://doi.org/10.1145/3313831.3376732
107.
WangF. H. (2022). A feasible study of a deep learning model supporting human–machine collaborative learning of object-oriented programming. IEEE Transactions on Learning Technologies, 17, 413–427. https://doi.org/10.1109/tlt.2022.3226345
108.
WangS.WalkerE. (2021). Providing adaptive feedback in concept mapping to improve reading comprehension. Paper presented at the Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–11). https://doi.org/10.1145/3411764.3445554
109.
WangZ.GongS.-Y.XuS.HuX.-E. (2019). Elaborated feedback and learning: Examining cognitive and motivational influences. Computers & Education, 136, 130–140. https://doi.org/10.1016/j.compedu.2019.04.003
110.
WildayaniW.MarhamiM.MursalinM.SaputraE.WulandariW. (2021). The improving mathematical reasoning ability of junior high school students using the constructivism approach assisted by animation videos. International Journal for Educational and Vocational Studies, 3(5), 330–338. https://doi.org/10.29103/ijevs.v3i5.5975
111.
WilliamsN. S.McArthurG. M.de WitB.IbrahimG.BadcockN. A. (2020). A validation of Emotiv EPOC Flex saline for EEG and ERP research. PeerJ, 8(2), e9713. https://doi.org/10.7717/peerj.9713
112.
WongwatkitC.SrisawasdiN.HwangG.-J.PanjabureeP. (2017). Influence of an integrated learning diagnosis and formative assessment-based personalized web learning approach on students learning performances and perceptions. Interactive Learning Environments, 25(7), 889–903. https://doi.org/10.1080/10494820.2016.1224255
113.
WorsleyM.AbrahamsonD.BliksteinP.GroverS.SchneiderB.TissenbaumM. (2016). Situating multimodal learning analytics. In Proceedings of International Conference of the Learning Sciences, ICLS (Vol. 2, pp. 1346–1349).
114.
YangA. C.FlanaganB.OgataH. (2022). Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning. Computers and Education: Artificial Intelligence, 3(4), 100104. https://doi.org/10.1016/j.caeai.2022.100104
115.
YangW.GuoA.LiY.QiuJ.LiS.YinS.RenY. (2018). Audio-visual spatiotemporal perceptual training enhances the P300 component in healthy older adults. Frontiers in Psychology, 9, 2537. https://doi.org/10.3389/fpsyg.2018.02537
116.
YangY. T. C.GambleJ. H.HungY. W.LinT. Y. (2014). An online adaptive learning environment for critical‐thinking‐infused E nglish literacy instruction. British Journal of Educational Technology, 45(4), 723–747. https://doi.org/10.1111/bjet.12080
117.
YinH. (2003). The research on concentration test and evaluation index for adolescence. China Sport Science and Technology, 39(3), 51–53.
118.
ZhangX.RäsänenP.KoponenT.AunolaK.LerkkanenM. K.NurmiJ. E. (2020). Early cognitive precursors of children’s mathematics learning disability and persistent low achievement: A 5‐year longitudinal study. Child Development, 91(1), 7–27. https://doi.org/10.1111/cdev.13123
119.
ZhengL.CuiP.LiX.HuangR. (2018). Synchronous discussion between assessors and assessees in web-based peer assessment: Impact on writing performance, feedback quality, meta-cognitive awareness and self-efficacy. Assessment & Evaluation in Higher Education, 43(3), 500–514. https://doi.org/10.1080/02602938.2017.1370533
120.
ZhongX.ZhanZ. (2024). An intelligent tutoring system for programming education based on informative tutoring feedback: System development, algorithm design, and empirical study. Interactive Technology and Smart Education. https://doi.org/10.1108/itse-09-2023-0182
121.
ZhuM.UrhahneD.Rubie-DaviesC. M. (2018). The longitudinal effects of teacher judgement and different teacher treatment on students’ academic outcomes. Educational Psychology, 38(5), 648–668. https://doi.org/10.1080/01443410.2017.1412399
