The　rapid　advancements　in　computer　vision　technology　present　significant　potential　for　the　automatic　recognition　of　learner　engagement　in　E-learning.　We　conducted　a　two-stage　experiment　to　assess　learner　engagement　based　on　behavioural　(external　observations)　and　physiological　(internal　factors)　cues.　Using　computer　vision　technology　and　wearable　sensors,　we　extracted　three　feature　sets:　action,　head　posture　and　heart　rate　variability　(HRV).　Subsequently,　we　integrated　our　constructed　YOLOv5s-MediaPipe　behaviour　detection　model　with　a　physiological　detection　model　based　on　HRV　to　comprehensively　evaluate　learners＇　behavioural,　affective　and　cognitive　engagement.　Additionally,　we　developed　a　method　and　criteria　for　assessing　distraction　based　on　behaviour,　ultimately　creating　a　comprehensive,　efficient,　low-cost　and　easy-to-use　system　for　the　automatic　recognition　of　learner　engagement.　Experimental　results　showed　that　our　improved　YOLOv5s　model　achieved　a　mean　average　precision　of　92.2　%,　while　halving　both　the　number　of　parameters　and　model　size.　Unlike　other　deep　learning-based　methods,　using　MediaPipe-OpenCV　for　head　posture　analysis　offers　advantages　in　real-time　performance,　making　it　lightweight　and　easy　to　deploy.　Our　proposed　long　short-term　memory　classifier,　based　on　sensitive　HRV　metrics　and　their　normalisation,　demonstrated　satisfactory　performance　on　the　test　set,　with　an　accuracy　=　80　%,　precision　=　81　%,　recall　=　80　%　and　an　F1　score　=　80　%.